JP2005031908A - Time series pattern generating apparatus and time series pattern generation method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To automatically generate an unique time series pattern which can be discriminated from other time series for the status of each time series. <P>SOLUTION: A time series pattern generating part 103 generates time series patterns which are not similar to each other to such an extent as shown by a similarity parameter by using time series data corresponding to the plurality of statuses of an object to be observed, and outputs the time series pattern obtained by erasing a time series pattern whose frequency of appearance is low from among those generated time series patterns as an output time series pattern 104. A time series pattern evaluating part 105 performs the overall evaluation of the output time series pattern 104 generated by the time series pattern generating part 103, and when the overall evaluation is within a predetermined standard, the time series pattern evaluating part 105 evaluates each time series pattern, and outputs the time series pattern obtained by excluding a time series pattern which does not satisfy an individual reference as a final time series pattern 107. Whereas a time series pattern evaluating part 105 corrects the similarity parameter when the above overall evaluation is beyond the predetermined reference, and transmits it to the time series pattern generating part 103. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

を用いて、後述する各時系列パターン発生モデル４０９（途中段階としての時系列パターン）に対する１セグメントデータ４０３の尤度（適合度）をそれぞれ計算する。
【００７３】
ここで、（式１）において、ｌ_ｉ、Δｈ_ｉ、ｙ_ｉ（ｔ）、μ_ｌｉ、μ_Δｈｉ、σ_ｌｉ及びσ_Δｈｉは、時系列パターン発生モデル４０９のパラメータである。より詳しくは、ｌ_ｉは、長さ、Δｈ_ｉは高さ、ｙ_ｉ（ｔ）は形状データを示す。また、後述から明らかになるように、μ_ｌｉ及σ_ｌｉは、長さの平均及び標準偏差、μ_Δｈｉ及びσ_Δｈｉは、高さの平均及び標準偏差を示す。一方、ｌ’_ｉ、Δｈ’_ｉ、ｙ’_ｉ（ｔ）は、１セグメントデータ４０３のパラメータである。また、ｇ_{μ ， σ}（ｘ）は平均をμ、標準偏差をσとした正規分布関数を表す。
【００７４】
（式１）に示すように、この尤度関数は、１セグメントデータ４０３と各時系列パターン発生モデルとの適合度を、長さ、高さ及び形状の観点から求める。具体的には、尤度関数は、Σ内に３つの乗算項を有しており、一番左の乗算項が長さの適合度、２番目の乗算項が高さの適合度、３番目の乗算項が形状の適合度に対応する。形状の適合度は、１セグメントデータ４０３の形状データと時系列パターン発生モデル４０９の形状データをそれぞれ正規化した状態で（長さと高さの影響を排除した基準となるサイズに修正した状態で）求めている。従って、（式１）によれば、長さと高さが多少異なっていても、形状が類似していれば、高い適合性を示す尤度が算出される。但し、１セグメントデータ４０３の長さあるいは高さが時系列パターン発生モデル４０９とがあまりに大きく異なる場合は、形状が類似していても、一番左の乗算項と２番目の乗算項によって、低い適合性を示す尤度が算出される。
【００７５】
モデル尤度計算部４０４は、（式１）を用いて算出した尤度のうち最大尤度と２番目の尤度を尤度データ４０８として、図４に示すように、１セグメントデータ４０３とともに、次の尤度評価部４０５に送出する。
【００７６】
尤度評価部４０５は、モデル尤度計算部４０４で算出された最大尤度（ｍａｘＱ_ｉ）と２番目の尤度（ｓｅｃｏｎｄＱ_ｉ）を元に、次の（式２）に示す尤度比算出式
【数２】

によって算出される尤度比Ｒを計算する。
【００７７】
尤度評価部４０５は、算出した尤度比Ｒ＝ｍａｘＱ_ｉ／ｓｅｃｏｎｄＱ_ｉを、入力された類似度パラメータ１０６（１セグメントデータ４０３と時系列パターン発生モデルとの類似度を判別する際の判別基準値）とを比較する。尤度評価部４０５は、尤度比Ｒ＝ｍａｘＱ_ｉ／ｓｅｃｏｎｄＱ_ｉが類似度パラメータ１０６以下であると判断した場合は、この１セグメントデータに類似する時系列パターン発生モデル４０９は存在しないと判断し、この１セグメントデータ４０３を新規モデル作成部４０７に送出する。
【００７８】
新規モデル作成部４０７は、受け取った１セグメントデータ４０３の長さ、高さ及び形状データと、長さおよび高さの平均（ここでは初期値として１セグメントデータの長さと高さそのものを用いる）と、長さ及び高さの標準偏差（ここでは初期値として０または既に得られている標準偏差の平均の３０％など）とからなる、時系列パターン発生モデル４０９を作成する。新規モデル作成部４０７は、作成した時系列パターン発生モデル４０９を図示しないバッファに記憶するとともに、継続指示データ４１２をセグメント取出継続評価部４０１に送出して、セグメント継続評価部４０１に処理を移行する。
【００７９】
一方、尤度評価部４０５は、算出した尤度比Ｒ＝ｍａｘＱ_ｉ／ｓｅｃｏｎｄＱ_ｉが類似度パラメータ１０６よりも大きいと判断した場合は、この１セグメントデータ４０３は、最大尤度ｍａｘＱ_ｉを持つ時系列パターン発生モデル４０９と類似していると判断する。尤度評価部４０５は、１セグメントデータ４０３と類似情報４１０（いずれの時系列パターン発生モデルに類似しているかを示す情報）とをモデル再学習部４０６に送出して、モデル再学習部４０６へ処理を移行する。
【００８０】
モデル再学習部４０６は、類似情報４１０に指定された時系列パターン発生モデルを図示しないバッファから取り出し、以下の（式３）に示す平均・標準偏差更新関数に従って、この時系列パターン発生モデルのパラメータを学習（更新）する。
【数３】

【００８１】
（式３）に示す通り、モデル再学習部４０６は、形状の学習を行わず、長さの平均と標準偏差、及び、高さの平均と標準偏差のみを更新する。モデル再学習部４０６は、更新後の時系列パターン発生モデル４０９を用いて、図示しないバッファ内の対応する時系列パターン発生モデル４０９を置き換える。一方、モデル再学習部４０６は、継続指示データ４１２をセグメント取出継続評価部４０１に送出して、セグメント取出継続評価部４０１に処理を移行する。
【００８２】
以下、本モデル群生成部３０６による処理動作について、セグメント長が１の生成時系列パターン３０７（図７（ａ）参照）を生成する場合を例に説明する。
【００８３】
まず、図４に示すように、セグメント取出継続評価部４０１は、セグメントデータ３０５（歩行時のもの）内に取り出すべき１セグメントデータ４０３が存在するか否かを判断する。ここでは、セグメント取出継続評価部４０１は、取り出すべき１セグメントデータ４０３は存在すると判断し、１セグメント取出部４０２に処理を移行する。
【００８４】
１セグメント取出部４０２は、セグメントデータ３０５から１セグメントデータ４０３（図６における形状ｙ１の区間参照）を取り出し、モデル尤度計算部４０４に送出する。
【００８５】
モデル尤度計算部４０４は、時系列パターン発生モデル４０９が図示しないバッファ内にまだ存在しないので（尤度比Ｒを算出するためには少なくとも２つの時系列パターン発生モデルが必要）、尤度を算出することなく、受け取った１セグメントデータ４０３をそのまま尤度評価部４０５に送出する。
【００８６】
尤度評価部４０５も、受け取った１セグメントデータ４０３をそのまま新規モデル作成部４０７に送出する。
【００８７】
新規モデル作成部４０７は、受け取った１セグメントデータ４０３を用いて、形状データと、長さ及び高さと、長さおよび高さの平均及び標準偏差とを第１の時系列パターン発生モデル４０９として生成し、図示しないバッファ内に記憶する。また、新規モデル作成部４０７は、セグメント取出継続評価部４０１に継続指示データ４１２を送出する。
【００８８】
継続指示データ４１２を受け取ったセグメント取出継続評価部４０１は、セグメントデータ３０５（図６参照）内にまだ取り出すべき１セグメントデータ４０３が存在するか否かを判断する。ここでは、セグメント取出継続評価部４０１は、取り出すべき１セグメントデータ４０３はまだ存在すると判断し、１セグメント取出部４０２に処理を移行する。
【００８９】
１セグメント取出部４０２は、セグメントデータ３０５から次の１セグメントデータ４０３（図６における形状ｙ２の区間参照）を取り出して、モデル尤度計算部４０４に送出する。
【００９０】
モデル尤度計算部４０４は、上述同様、１セグメントデータ４０３をそのまま尤度評価部４０５に送出する。
【００９１】
尤度評価部４０５も、受け取った１セグメントデータ４０３をそのまま新規モデル作成部４０７に送出する。
【００９２】
新規モデル作成部４０７は、上述と同様にして、受け取った１セグメント４０３を用いて、第２の時系列パターン発生モデル４０９を生成して、図示しないバッファ内に記憶するとともに、セグメント取出継続評価部４０１に継続指示データ４１２を送出する。
【００９３】
継続指示データ４１２を受け取ったセグメント取出継続評価部４０１は、セグメントデータ３０５（図６参照）内にまだ取り出すべき１セグメントデータが存在するか否かを判断する。ここでは、セグメント取出継続評価部４０１は、取り出すべき１セグメントデータ４０３はまだ存在すると判断し、１セグメント取出部４０２に処理を移行する。
【００９４】
１セグメント取出部４０２は、セグメントデータ３０５内からさらに次の１セグメントデータ４０３（図６における形状ｙ３の区間参照）を取り出して、モデル尤度計算部４０４に送出する。
【００９５】
モデル尤度算出部４０４は、上述した図示しないバッファ内における第１の時系列パターン発生モデル４０９と、１セグメントデータとを尤度関数（式１）に入力して第１の時系列パターン発生モデル４０９に対する尤度を求める。また、モデル尤度算出部４０４は、上述した第２の時系列パターン発生モデル４０９と１セグメントデータとを尤度関数（式１）に入力して第２の時系列パターン発生モデル４０９に対する尤度を求める。モデル尤度算出部４０４は、算出した第１及び第２の時系列パターン発生モデル４０９に対する尤度（尤度データ４０８）と、１セグメント取出部４０２から受け取った１セグメントデータ４０３とを、尤度評価部４０５に送出する。
【００９６】
尤度評価部４０５は、（式２）に従って、尤度データ４０８を用いて、尤度比Ｒを算出し、算出した尤度比Ｒを、入力された類似度パラメータ１０６と比較する。尤度評価部４０５は、ここでは、尤度比Ｒは類似度パラメータ１０６以下であると判断し（第１及び第２の時系列パターン発生モデル４０９のいずれにも類似していないと判断し）、１セグメントデータ４０３を新規モデル作成部４０７に送出する。
【００９７】
新規モデル作成部４０７は、上述と同様にして、受け取った１セグメントデータ４０３を用いて、第３の時系列モデル発生モデル４０９を生成して図示しないバッファ内に記憶するとともに、セグメント取出継続評価部４０１に継続指示データ４１２を送出する。
【００９８】
継続指示データ４１２を受け取ったセグメント取出継続評価部４０１は、セグメントデータ３０５（図６参照）内にまだ取り出すべき１セグメントデータ４０３が存在するか否かを判断する。ここでは、セグメント取出継続評価部４０１は、取り出すべき１セグメントデータ４０３はまだ存在すると判断し、１セグメント取出部４０２に処理を移行する。
【００９９】
１セグメント取出部４０２は、セグメントデータ３０５内からさらに次の１セグメントデータ４０３（図６における形状ｙ４の区間参照）を取り出し、モデル尤度計算部４０４に送出する。
【０１００】
モデル尤度算出部４０４は、上述と同様に、第１、第２および第３の時系列パターン発生モデルに対する１セグメントデータ４０３の尤度を、尤度関数（式１）を用いて算出する。モデル尤度算出部４０４は、算出した３つの尤度のうち１番目及び２番目に大きいもの（尤度データ４０８）と、１セグメント取出部４０２から受け取った１セグメントデータ４０３とを、尤度評価部４０５に送出する。
【０１０１】
尤度評価部４０５は、受け取った尤度データ４０８を用いて、（式２）に従って、尤度比Ｒを算出する。尤度評価部４０５は、算出した尤度比Ｒと類似度パラメータ１０６とを比較する。尤度評価部４０５は、ここでは尤度比Ｒは類似度パラメータ１０６より大きいと判断する。尤度評価部４０５は、最大尤度を持つ時系列パターン発生モデル（ここでは第１の時系列パターン発生モデルとする）と１セグメントデータ４０３とは類似していると判断する。従って、尤度評価部４０５は、１セグメントデータ４０３と類似情報４１０とをモデル再学習部４０６に送出する。
【０１０２】
モデル再学習部４０６は、類似情報４１０に基づいて第１の時系列パターン発生モデル４０９を特定し、この第１の時系列パターン発生モデル４０９と１セグメントデータ４０３とを用いて、上述の（式３）に従って、第１の時系列パターン発生モデル４０９のパラメータを学習（更新）する。上述したように、形状の学習は行わず、長さと高さの平均及び標準偏差のみを更新する。パラメータを更新したモデル再学習部４０６は、セグメント取出継続評価部４０１に継続指示データ４１２を送出する。
【０１０３】
以上に説明した処理を、セグメントデータ３０５内の残りの他の１セグメントデータ４０３（図６における形状ｙ５の区間参照）および走行時における各１セグメントデータ４０３（図６における形状ｙ６〜ｙ１０の区間参照）についても行い、最終的に、パターン長が１である５つの時系列パターン発生モデル４０９（図７（ａ）参照）が取得される（図示しないバッファ内に格納される）。そして、セグメント取出継続評価部４０１は、セグメントデータ３０５内に取り出すべき１セグメントデータが存在しないと判断したら、上で取得された５つの時系列パターン発生モデル４０９をそれぞれ時系列パターンとして確定し、これらを生成時系列パターン３０７として出力する（図７（ａ）参照）。上述から分かるように、生成時系列パターン３０７を構成する各時系列パターンは、新規に時系列パターン発生モデルを生成する元となった１セグメントデータの長さ、高さ、形状と、上述した、長さ及び高さの平均及び標準偏差とを有する。なお、図７、図８、図９、図１１及び図１２に示される各時系列パターンに付された長さｌ、高さΔｈ及び形状ｙは、ここでは、この１セグメントデータの長さ、高さ、形状とする。
【０１０４】
以上では、パターン長が１の場合におけるモデル群生成部３０６の処理動作について説明したが、パターン長が２，３・・・の場合も同様にして行われる。
【０１０５】
次に、図１に示すように、時系列パターン評価部１０５について説明する。
【０１０６】
図２は、時系列パターン評価部１０５の構成を詳細に示す図である。
【０１０７】
上述したように、時系列パターン評価部１０５は、出力時系列パターン１０４が所望の基準を満たすか否かを判断し、満たす場合は、後述するようにして最終時系列パターン１０７を出力し、一方、満たさない場合は、類似度パラメータ１０６を補正する。
【０１０８】
図２に示すように、この時系列パターン評価部１０５は、出現確率計算部２０１、時系列パターン評価値計算部２０３、時系列パターン評価値評価部２０５および類似度パラメータ補正部２０６を備える。
【０１０９】
出現確率計算部２０１は、出力時系列パターン１０４を構成する各時系列パターン（図９参照）が、各時系列の状態（例えば歩行および走行）において出現する頻度（出現頻度）をそれぞれ調べる。そして、出現確率計算部２０１は、調べた結果を元に、各時系列パターンが、各状態において出現する確率（出現確率）を計算し、算出結果を出現確率２０２として時系列パターン評価値計算部２０３に送出する。
【０１１０】
図１０は、図９の各時系列パターン１〜５，１２，４５（出力時系列パターン１０４）について算出した各状態における出現頻度（歩行時頻度及び走行時頻度）、各状態における出現確率２０２（歩行時確率及び走行時確率）及び時系列パターン評価値２０４（時系列パターン個別評価値の集合）を示した図表である。
【０１１１】
図１０に示すように、各時系列パターン１〜５、１２、４５（図９参照）が、歩行時及び走行時のどちらに頻繁に現れるのかが分かる。例えば、時系列パターン１は、歩行時頻度が２、走行時頻度が０で、従って、歩行時確率は１、走行時確率は０である。
【０１１２】
時系列パターン評価値計算部２０３は、出現確率計算部２０１から受け取った出現確率２０２を用いて、各時系列パターンを評価するための時系列パターン個別評価値をそれぞれ算出し、算出した時系列パターン個別評価値を時系列パターン評価値２０４として時系列パターン評価値評価部２０５に送出する。ここで、時系列パターン個別評価値は、各時系列パターンの各状態への偏り具合状況を数値化して表したものである。
【０１１３】
図１０に示す各時系列パターン個別評価値は、各時系列パターンの平均情報量（エントロピ）を元に算出したものである。この評価では、各時系列パターンは、平均情報量の値が低いほど各状態に偏った良い時系列パターンとなる。例えば、時系列パターン個別評価値が０のパターン１，２，４，５，１２，４５は、歩行時および走行時のいずれかにのみ出現する好ましい時系列パターンといえる。これに対し、時系列パターン個別評価値が０．１５０５１５のパターン３は、歩行時および走行時にそれぞれ同等の確率で出現しており、好ましくない時系列パターンであるといえる。
【０１１４】
時系列パターン評価値評価部２０５は、時系列パターン評価値計算部２０３から受け取った時系列パターン評価値２０４と入力された出力時系列パターン１０４とを用いて、出力時系列パターン１０４の評価を行う。
【０１１５】
より詳しくは、時系列パターン評価値評価部２０５は、時系列パターン評価値２０４を用いて出力時系列パターン１０４全体が所望の基準を満たすか否か評価するための時系列パターン全体評価値を算出する。
【０１１６】
時系列パターン評価値評価部２０５は、算出した時系列パターン全体評価値が所定の基準内にあれば、時系列パターン個別評価値が所定の基準外にある時系列パターンを除去したものを最終時系列パターン１０７として出力する。
【０１１７】
一方、時系列パターン評価値評価部２０５は、算出した時系列パターン全体評価値が所定の基準外ならば、時系列パターン評価値２０４を類似度パラメータ補正部２０６へ送出する。
【０１１８】
以下、出力時系列パターン１０４の評価について具体的に説明する。
【０１１９】
まず、図９〜図１１を用いて、時系列パターン全体評価値が所定の基準内になる場合について説明する。
【０１２０】
時系列パターン評価値評価部２０５は、まず、時系列パターン評価値２０４の平均を算出し、これを時系列パターン全体評価値とする。時系列パターン評価値評価部２０５は、算出した時系列パターン全体評価値と、ユーザ等により設定された全体設定許容値０．１１（ユーザにとって納得のいく出力時系列パターン１０４の水準を示し、好適には例えば０．１５以下とする）とを比較する。つまり、時系列パターン評価値評価部２０５は、図１０の時系列パターン評価値２０４の平均値０．０２２（＝０．１５０５１５／７）と、全体設定許容値０．１１とを比較する。平均値０．０２２は、全体設定許容値０．１１よりも小さいので、時系列パターン評価値評価部２０５は、時系列パターン全体評価値は所定の基準内にあると判断する。時系列パターン評価値評価部２０５は、出力時系列パターン１０４を構成する各時系列パターン（図９参照）のうち、個別設定許容値（ユーザにとって納得のいく、各時系列パターンを評価する水準であり、ここでは全体設定許容値と同じ値０．１１を有する）よりも高い時系列パターン個別評価値を有する時系列パターン（図１０参照）が存在するか否かを判断する。そして、時系列パターン評価値評価部２０５は、そのような時系列パターンが存在すると判断した場合は、図９と図１１とを比較して分かるように、その時系列パターン（図１０の時系列パターン３）を出力時系列パターン１０４から除去する。そして、時系列パターン評価値評価部２０５は、除去後の出力時系列パターン１０４（図１１参照）を最終時系列パターン１０７として出力する（図２参照）。
【０１２１】
上述では、全体設定許容値及び個別設定許容値としてそれぞれ同じ値を用いたが、それぞれ異なる値を設定してもよい。
【０１２２】
次に、図１２および図１３を用いて、時系列パターン評価値２０４から算出された時系列パターン全体評価値が所定の基準外になる場合について説明する。
【０１２３】
図１２は、上述とは異なる値（上述よりも緩い値）を有する類似度パラメータ１０６によって時系列パターン生成部１０３（図１参照）において生成された出力時系列パターン１０４例を示す図である。この出力時系列パターン１０４は、上述と同様に、図５の時系列データ１０２から生成されたものである。
【０１２４】
図１２に示すように、図９では異なる時系列パターンとして認識されていた時系列パターン１２，４５（図９参照）が、ここでは同一の時系列パターン１２として認識されている。同様に、図９では異なる時系列パターンとして認識されていた時系列パターン１，４が、ここでは同一のパターン１として、また、図９では異なる時系列パターンとして認識されていた時系列パターン２，５が、ここでは、同一の時系列パターン２として認識されている。
【０１２５】
図１３は、図１２の各時系列パターンに対して上述と同様にして算出した各状態における出現頻度、各状態における出現確率２０２及び時系列パターン評価値２０４を示した図表である。
【０１２６】
ここで、出力時系列パターン１０４（図１２参照）の評価について説明すると以下の通りである。
【０１２７】
上述と同様に、まず、時系列パターン評価値評価部２０５（図２参照）は、時系列パターン評価値２０４の平均（時系列パターン全体評価値）と、全体設定許容値０．１１（所定の基準）とを比較する。時系列パターン評価値２０４の平均（時系列パターン全体評価値）は、図１３から分かるように、０．１５０５１５であり、全体設定許容値０．１１よりも大きい。従って、時系列パターン評価値評価部２０５は、時系列パターン全体評価値は所定の基準外にあると判断する。つまり、時系列パターン評価値評価部２０５は、図１２の出力時系列パターン１０４は、目的を満たしていないと判断し、図２に示すように、時系列パターン評価値２０４を類似度パラメータ補正部２０６に送出する。
【０１２８】
類似度パラメータ補正部２０６は、時系列パターン評価値評価部２０５から受け取った時系列パターン評価値２０４を用いて類似度パラメータ２０６値を補正する（より厳しいものにする）。補正の例としては、時系列パターン評価値２０４の平均の前回との差分（初期値は例えば０とする）と、類似度パラメータ１０６の前回との差分（初期値は０とする）との比を定数倍し、今回の類似度パラメータ１０６からこれを減ずるなどがある。
【０１２９】
類似度パラメータ補正部２０６は、以上のようにして算出した補正後の類似度パラメータ１０６を、図１に示すように、時系列パターン生成部１０３に送出する。
【０１３０】
以上に説明したように、本実施の形態によれば、時系列データから、類似度パラメータで示される程度に相互に類似しない時系列パターンを、形状が複雑なものも含めて、パターン長を固定することなく、生成することができる。
【０１３１】
また、時系列データに加えて、時系列の状態も用いるようにしたので、各状態について、できるだけ他の状態には出現しない時系列パターンを生成することが可能となる。
【０１３２】
また、目的の基準に沿った時系列パターンが得られるまで、類似度パラメータを何度でも補正して、最終的に、時系列の状態全てに対して固有の時系列パターンを得ることができる。この際、効率よく類似パラメータを補正することで、より早く目的の基準に沿った時系列パターンを生成することが可能になる。
【０１３３】
また、生成された時系列パターンの各状態における出現確率を算出するようにしたので、時系列パターン個別評価値として、例えば平均情報量を用いることが可能となる。
【０１３４】
また、出現頻度が低い時系列パターンについては削除するようにしたので有効な時系列パターンの生成が可能となる。
【０１３５】
また、削除後残存時系列パターン内の各時系列パターンに対応する時系列データを含むように、パターン長増加後におけるセグメントデータの切出しを行うようにしたので、あまり重要でないセグメントデータの切出しを無視して、効率のよいセグメントデータの切出しが可能になる。
【０１３６】
また、セグメント情報（長さ及び高さ）のみではなく、正規化した形状をも用いて、時系列パターン発生モデルに対する尤度を計算するようにしたので、セグメントデータの形状が複雑な形状を保持していても、適正な類否判別が可能となる。つまり、セグメントデータが複雑な形状を保持していても、相互に類似しない時系列パターンを、長さと高さの曖昧さを吸収して、時系列データから効率よく生成することができる。
【０１３７】
また、得られた時系列パターンが目的に合うかどうかを評価し、目的に沿わない時系列パターンは除去するようにしたので、目的に沿った時系列パターンのみを確実に抽出することができる。
【０１３８】
また、各時系列の状態に偏った時系列パターンを抽出できるので、時系列パターンによるクラス分類も行うことができる。
【０１３９】
また、時系列パターン全体評価値（時系列パターン個別評価値の平均）と全体設定許容値（所定の基準）とを比較して出力時系列パターンを評価するようにしたので、ユーザにとって理解しやすい閾値設定を行うことが可能になる。
【０１４０】
また、各状態における時系列パターンの出現確率や、時系列パターン全体評価値、時系列パターン個別評価値等を画面上等に表示することで、ユーザが時系列パターンを自ら選択することもできる。
【０１４１】
【発明の効果】
本発明により、各状態に対して他の状態と区別することができる固有の時系列パターンを各状態から得られた時系列データに基づき自動的に生成できる。
【図面の簡単な説明】
【図１】本発明の一実施形態に関わる時系列パターン生成装置の構成例を示す図。
【図２】時系列パターン生成装置における時系列パターン評価値評価部の構成例を示す図。
【図３】時系列パターン生成装置における時系列パターン生成部の構成例を示す図。
【図４】時系列パターン生成部におけるモデル群生成部の構成例を示す図。
【図５】状態データおよび時系列データを説明するための図。
【図６】セグメント情報、セグメントデータおよび１セグメントデータを説明するための図。
【図７】生成時系列パターンの例を示す図。
【図８】削除後残存時系列パターンの例（パターン長が２の場合）を示す図。
【図９】出力時系列パターンの例を示す図。
【図１０】出現頻度、出現確率および時系列パターン個別評価値の例を示す図表。
【図１１】最終時系列パターンの例を示す図。
【図１２】途中段階での出力時系列パターンの例を示す図。
【図１３】途中段階での出現確率および時系列パターン個別評価値の例を示す図表。
【符号の説明】
１０１ 状態データ
１０２ 時系列データ
１０３ 時系列パターン生成部
１０４ 出力時系列パターン
１０５ 時系列パターン評価部
１０６ 類似度パラメータ
１０７ 最終時系列パターン
２０１ 出現確率計算部
２０２ 出現確率
２０３ 時系列パターン評価値計算部
２０４ 時系列パターン評価値
２０５ 時系列パターン評価値評価部
２０６ 類似度パラメータ補正部
３０１ セグメント化部
３０２ セメント情報
３０３ セグメント切出し部
３０５ セグメントデータ
３０６ モデル群生成部
３０７ 生成時系列パターン
３０８ 時系列パターン削除部
３０９ 削除後残存時系列パターン
３１１ 継続評価部
３１２ 継続処理部
４０１ セグメント取出継続評価部
４０２ １セグメント取り出し部
４０３ １セグメントデータ
４０４ モデル尤度計算部
４０５ 尤度評価部
４０６ モデル再学習部
４０７ 新規モデル作成部[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a time-series pattern generation apparatus and a time-series pattern generation method for generating a time-series pattern specific to each state from time-series data corresponding to a plurality of states to be observed, and time series data that are not similar to each other. The present invention relates to a time series pattern generation method for generating a series pattern.
[0002]
[Prior art]
With the improvement of sensing technology and processor technology, sensors have become familiar with the reduction in cost. For example, it has become possible to attach a sensor to the foot and acquire the operation as time-series data.
[0003]
Therefore, it is becoming possible to attach a sensor to a person to acquire time series data, and to generate a characteristic time series pattern that appears when the person performs some action from the acquired time series data. Yes. In addition, in order to identify what kind of action the person is doing, the time series data obtained from each action (time series state) is used to generate a time series pattern specific to each action. Has also become required.
[0004]
However, in general, the time series pattern to be acquired often has a huge amount, and there is a problem that it takes a lot of labor to search for the time series pattern by hand.
[0005]
Also, there is no known method for generating a time series pattern specific to each operation using the above-described latter, that is, time series data obtained from each state.
[0006]
On the other hand, with respect to the former, that is, to generate a time-series pattern characteristic of this state using time-series data obtained from a certain state, as a method for realizing this, for example, time-series clustering and rule analysis (Non-Patent Document 2) is extended in time series.
[0007]
However, in any case, it is necessary to divide the time series data into segments (segmentation) according to an appropriate standard for processing, but in the former time series clustering, the number of segments (number of segments), that is, the pattern length is constant. There is a drawback that it is necessary to be (fixed).
[0008]
On the other hand, the latter rule analysis has the advantage that the pattern length is automatically determined and the time series pattern can be generated efficiently. However, when generating a time series pattern with a long pattern length, the time series with a short pattern length is used. A long time series pattern is expressed by combining patterns. For this reason, there exists a fault which each time series pattern tends to be averaged. This is because a short time-series pattern is simple in shape, and thus there are many approximate time-series patterns in many cases. Therefore, this method has a drawback that it cannot handle time-series patterns having a complicated shape when the pattern length becomes long.
[0009]
By the way, when generating a time series pattern, it is preferable that a lot of similar time series patterns are not generated. Therefore, it is necessary to determine similarity of the extracted time series data. In the similarity determination, there is a request that it is the same if the shape is similar even if the length and height are different (to absorb the ambiguity of the length and height). There was a drawback that only the length ambiguity could be absorbed. On the other hand, there is a method using a time series pattern generation model (Non-patent Document 1) as a method that can absorb both ambiguity in length and height, but this method regards a straight line without retaining the shape of the time series. For this reason, there is a disadvantage that complicated shapes cannot be distinguished from each other.
[0010]
[Patent Document 1]
JP 11-326542 A
[Patent Document 2]
JP-A-9-34719
[Non-Patent Document 1]
Xianping Ge and Padhric Smith, Deformable Markov Model Templates for Time-Series Pattern Matching, ACM SIGKDD (KDD-2000), pp. 81-90, 2000
[Non-Patent Document 2]
Rakesh Agrawal, Tomasz Imielinski, Arun N .; Swami, Mining Association Rules between Sets of Items in Large Databases, SIGMOD Conference 1993, pp. 207-216, 1993
[0011]
[Problems to be solved by the invention]
As described above, conventionally, there has been a problem in that a time series pattern that can be distinguished from other time series states cannot be automatically generated for each time series state. In addition, when the pattern length is increased, there is a problem that a complicated shape cannot be handled. Furthermore, there is a problem that it is impossible to determine the similarity of a complicated shape that absorbs the ambiguity of length and height.
[0012]
The present invention has been made in consideration of the above-described circumstances, and each time-series state can be distinguished from other time-series states, and a time-series pattern unique to each time-series state is automatically generated. An object of the present invention is to provide a time-series pattern generation device and a time-series pattern generation method that can be generated easily. It is another object of the present invention to provide a time-series pattern generation method that can handle a complicated shape even when the pattern length increases. Furthermore, an object of the present invention is to provide a time-series pattern generation method that makes it possible to determine the similarity of complex shapes, thereby taking into account complex shapes in addition to length and height.
[0013]
[Means for Solving the Problems]
The time-series pattern generation device of the present invention is a time-series pattern generation device that generates a time-series pattern specific to each state using time-series data corresponding to a plurality of states to be observed, the plurality of time-series patterns A plurality of partial data groups having the same similarity from the plurality of partial data are extracted using a similarity determination criterion for cutting out a plurality of partial data from the series data and determining the similarity between the partial data. A time series pattern generation unit that extracts and generates a time series pattern corresponding to each of the plurality of partial data groups, and whether the time series pattern generated by the time series pattern generation unit conforms to a predetermined standard If within the predetermined criteria, the relationship between each of the time series patterns and the frequency of appearance of each of the time series patterns in each time series data The time series pattern is specified as one of the states based on the output time, and the specified time series pattern is output as a final time series pattern. And the corrected similarity determination criterion is output to the time-series pattern generation unit in order to cause the time-series pattern generation unit to generate the time-series pattern again using the corrected similarity determination criterion. And a time-series pattern evaluation unit.
[0014]
The time-series pattern generation method of the present invention is a time-series pattern generation method for generating a time-series pattern specific to each state using time-series data corresponding to a plurality of states to be observed. A plurality of partial data groups having the same similarity from the plurality of partial data are extracted using a similarity determination criterion for cutting out a plurality of partial data from the series data and determining the similarity between the partial data. A time series pattern generation step for extracting and generating a time series pattern corresponding to each of the plurality of partial data groups, and whether or not the time series pattern generated by the time series pattern generation step conforms to a predetermined standard Each time series pattern and each time series pattern occurrence in each time series data if within the predetermined criteria Based on the relationship with the degree, the time series pattern is specified as any of the states, and the specified time series pattern is output as a final time series pattern. A time series pattern evaluation step of correcting the similarity determination criterion and performing the time series pattern generation step again according to the corrected similarity determination criterion.
[0015]
The time-series pattern generation method of the present invention is a time-series pattern generation method for generating time-series patterns that are not similar to each other from time-series data, and the time-series data is divided into several segments based on a certain reference, and a specific segment From the plurality of partial data, using a partial data cutting step for cutting out each of the partial data from each of the time series data in a long unit and a similarity determination criterion for determining the similarity between the partial data The time series pattern corresponding to each of the plurality of partial data groups in a state in which a plurality of partial data groups having the same similarity relation are extracted and the number of partial data included in the partial data group is included as an appearance frequency From a model group generation step for generating the model group and the time series pattern generated by the model group generation step. A time-series pattern deletion step of deleting the time-series pattern having the appearance frequency equal to or lower than a threshold value, and evaluating the number of the time-series patterns remaining after the deletion to determine whether to continue or not. The time-series pattern remaining after deletion is stored, and the segment length is changed to perform the steps and the main step. On the other hand, in the case of non-continuation, the time-series pattern for each stored segment length is output. And a continuous evaluation step.
[0016]
The time series pattern generation method of the present invention is a time series pattern generation method for generating time series patterns that are not similar to each other using a plurality of partial data cut out from the time series data, and generates the time series pattern. A new model creation step for creating a time series pattern generation model to be a model, and a model relearning step for correcting the time series pattern generation model, and the likelihood of each of the partial data for the time series pattern generation model, Model likelihood calculation step for calculating from the viewpoint of the length and height of the partial data, and the shape excluding the influence of the length and the height by normalization, and calculation for each time series pattern generation model A similarity discriminator for discriminating between the likelihood and the similarity between the partial data and the time series pattern generation model A likelihood evaluation step for evaluating whether or not the time-series pattern generation model similar to the partial data exists using the similarity parameter as If not, perform the new model creation step to create the time series pattern generation model, and if there is a similar time series pattern generation model, the time series pattern generation model having the highest likelihood is A likelihood evaluation step for performing correction by performing the model relearning step, and determining whether or not to repeat the model likelihood calculation step and the likelihood evaluation step for the other partial data. The model likelihood calculation step and the likelihood evaluation step are performed on the other partial data and are not repeated. When determining is characterized by comprising a further processing step of each said time series pattern generation model of the final state is determined as each of said time-series pattern.
[0017]
DETAILED DESCRIPTION OF THE INVENTION
First, features of the present embodiment will be briefly described.
[0018]
FIG. 5 is a diagram showing time-series data 102 composed of time-series data obtained during walking and time-series data obtained during travel. This time-series data 102 is obtained from, for example, an acceleration sensor attached to a biological foot.
[0019]
In the figure, state data (label) 101 including a walking label and a traveling label is shown. The state data 101 is used to specify in what state the time-series data 102 has been acquired, as will be described later.
[0020]
In the present embodiment, a time series pattern specific to each state is generated using such time series data of each state (eg, walking, running).
[0021]
Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.
[0022]
FIG. 1 is a diagram illustrating a configuration example of a time-series pattern generation apparatus according to an embodiment of the present invention.
[0023]
As illustrated in FIG. 1, the time-series pattern generation device includes a time-series pattern generation unit 103 and a time-series pattern evaluation unit 105.
[0024]
The time-series pattern generation unit 103 uses the input state data 101 (see FIG. 5) and time-series data 102 (see FIG. 5) to have various pattern lengths that are not similar to each other (the pattern length is unfixed). Generate a time series pattern. Generation of time series patterns that are not similar to each other is performed using a similarity parameter 106 (similarity criterion) described later. The time series pattern generation unit 103 outputs the generated time series pattern group as an output time series pattern 104.
[0025]
FIG. 9 to be described later is a diagram showing an example of a time series pattern generated from the time series data 102 of FIG. In FIG. 9, a time series pattern with a pattern length of 1 (upper stage) and a time series pattern with a pattern length of 2 (lower stage) are shown.
[0026]
The time series pattern evaluation unit 105 uses the output time series pattern 104 output from the time series pattern generation unit 103 to evaluate whether or not the output time series pattern 104 satisfies a desired criterion.
[0027]
More specifically, first, the time series pattern evaluation unit 105 individually evaluates the time series pattern overall evaluation value for evaluating the entire output time series pattern 104 and each time series pattern constituting the output time series pattern 104. The time series pattern individual evaluation value for this is calculated using a method described later.
[0028]
Next, the time-series pattern evaluation unit 105 determines whether or not the time-series pattern overall evaluation value is within a predetermined standard, and if it is determined that it is within the predetermined standard, next, It is determined whether or not the time series pattern individual evaluation value is within a predetermined standard. When the time-series pattern evaluation unit 105 determines that there is a time-series pattern individual evaluation value that is outside the predetermined reference, the time-series pattern evaluation unit 105 deletes the time-series pattern having the time-series pattern individual evaluation value from the output time-series pattern 104. The time series pattern evaluation unit 105 outputs the output time series pattern 104 in this state as a final time series pattern 107 as shown in FIG.
[0029]
On the other hand, when the time series pattern evaluation unit 105 determines that the overall time series pattern evaluation value is outside the predetermined reference, the above-described similarity is determined using the overall time series pattern evaluation value or each time series pattern individual evaluation value. The parameter 106 is corrected in a more severe direction. Then, as shown in FIG. 1, the time-series pattern evaluation unit 105 sends the corrected similarity parameter 106 to the time-series pattern generation unit 104 and again causes the time-series pattern generation unit 103 to correct the similarity after correction. The output time series pattern 104 is output by the parameter 106. The time series pattern evaluation unit 105 repeats the correction of the similarity parameter 106 until the time series pattern overall evaluation value falls within a predetermined standard.
[0030]
The final time series pattern 107 output by the time series pattern evaluation unit 105 is a time series pattern finally obtained by this method (for example, a time series pattern unique to walking, a time series pattern unique to running). An example of the final time series pattern 107 is shown in FIG. This is obtained by removing the time series pattern (pattern 3) in which the time series pattern individual evaluation value is determined to be outside the predetermined reference from the output time series pattern 104 of FIG. 9 as described above. As will be apparent from the description below, the patterns 1, 2, and 12 are time series patterns unique to walking, and the other patterns 4, 5, and 45 are time series patterns unique to running. Note that the time series pattern evaluation unit 105 outputs only some of the top ranks (for example, those having a high appearance frequency) when there are a large number of time series patterns whose time series pattern individual evaluation values are within a predetermined standard. Anyway.
[0031]
Next, the time series pattern generation unit 103 and the time series pattern evaluation unit 105 will be described in more detail.
[0032]
First, the time series pattern generation unit 103 will be described.
[0033]
FIG. 3 is a diagram showing in detail the configuration of the time series pattern generation unit 103.
[0034]
As described above, the time series pattern generation unit 103 generates the output time series pattern 104 using the time series data 102 and the state data 101.
[0035]
As shown in FIG. 3, the time series pattern generation unit 103 includes a segmentation unit 301, a segment cutout unit 303, a model group generation unit 306, a time series pattern deletion unit 308, a continuation evaluation unit 311, and a continuation processing unit 312. Prepare.
[0036]
The segmentation unit 301 divides the input time series data 102 into a plurality of sections (segments) on a predetermined basis (segmentation), and for each section, a section size (length in the case of a one-dimensional time series), Calculate the height of the section. The segmenting unit 301 outputs the section size and the section height for each section as segment information 302. As an example of segmentation, there is piecewise linear approximation (in the case of a one-dimensional time series, broken line).
[0037]
FIG. 6 is a diagram showing a state in which time-series data (see FIG. 5) during walking and running are segmented.
[0038]
In FIG. 6, the segment information 302 described above is all data of (length l, height Δh) = (l1, Δh1), (l2, Δh2),..., (L10, Δh10). Here, heights Δh1, Δh2,... Δh10 (not shown) are the differences between the maximum height and the minimum height in each corresponding section. As the height of the section, for example, a difference in height between the beginning and the end of the section may be taken.
[0039]
As shown in FIG. 3, the segment cutout unit 303 adds segment data 305 (state data is not shown) to each piece of data constituting the segment information 302 described above by adding shape data of each section and input state data. ) Is generated. In the example of FIG. 6, the segment data 305 includes (length l, height Δh, shape y) = (l1, Δh1, y1), (l2, Δh2, y2),..., (L10, Δh10, y10). ) Of all data (state data not shown). The shape data y1, y2... Includes the length and height of the corresponding section, but the length and height data are obtained separately from the shape data because they are used in calculations described later. . Each piece of data (length l, height Δh, shape y) (state data not shown) constituting the segment data 305 is referred to as one segment data 403.
[0040]
The model group generation unit 306 uses the segment data 305 generated by the segment cutout unit 303 and the similarity parameter 106 described later, and is not similar to each other, and a time-series pattern based on a specific pattern length (number of segments) (for each state) Including frequency of appearance in time series data). The model group generation unit 306 outputs the generated time series pattern group as a generation time series pattern 307.
[0041]
FIGS. 7A and 7B are diagrams showing an example of a generated time series pattern 307 generated from the segment data 305 (see FIG. 6).
[0042]
More specifically, FIG. 7A is a diagram illustrating an example of a generated time series pattern 307 generated when the pattern length is 1. As shown in the figure, here, five time series patterns 1 to 5 are generated. Here, for example, “Reference: y1, y4” of the time-series pattern 1 indicates that it corresponds to the shapes y1, y4 in FIG. That is, the pattern 1 is a time series pattern generated from the shapes y1 and y4.
[0043]
On the other hand, FIG. 7B is a diagram showing an example of a generation time series pattern when the pattern length is 2. As shown in the figure, when the pattern length is 2, six time-series patterns 12, 23, 31, 45, 53, and 34 are generated.
[0044]
As shown in FIG. 3, the time series pattern deletion unit 308 deletes time series patterns whose appearance frequency is equal to or less than the deletion threshold among the time series patterns in the generated time series pattern 307 and deletes the remaining time series patterns. The remaining time series pattern 309 is output. This is because the time series pattern with a low appearance frequency is deleted to increase the calculation efficiency in the continuation evaluation unit 311 described later.
[0045]
FIG. 8 is a diagram illustrating an example of a post-deletion remaining time series pattern 309 obtained when the deletion threshold is set to 1 with respect to the generation time series pattern 307 (see FIG. 7B) having a pattern length of 2. .
[0046]
As can be seen by comparing FIG. 7B and FIG. 8, time series patterns 23, 31, 53, and 34 having an appearance frequency of 1 are deleted from the six time series patterns shown in FIG. 7B. Then, the patterns 12 and 45 whose appearance frequency is 2 are output as the remaining time series pattern 309 after deletion.
[0047]
The continuous evaluation unit 311 examines the number of time series patterns in the post-deletion remaining time series pattern 309 output by the time series pattern deletion unit 308. If the continuous evaluation unit 311 determines that the number of time-series patterns in the remaining time-series pattern 309 after deletion is not 0, that is, if it is determined that there is a possibility that a time-series pattern having a larger pattern length can be generated, After the post-deletion remaining time series pattern 309 is stored, the process proceeds to the continuation processing unit 312.
[0048]
That is, the continuation processing unit 312 generates the segment data 305 having an increased pattern length (for example, increasing the pattern length from 1 to 2 or from 2 to 3) using the time-series data 102. At this time, the segment data 305 is acquired in a state including a portion of the time series data corresponding to the time series pattern in the remaining time series pattern 309 after deletion. This is to achieve efficient processing by suppressing the extraction of data that seems to be less important.
[0049]
On the other hand, if the continuation evaluation unit 311 determines that the number of time series patterns in the remaining time series pattern 309 after deletion is 0, that is, it is determined that there is no possibility of generating a time series pattern having a larger pattern length. In this case, the post-deletion remaining time series pattern 309 for each pattern length is output as the output time series pattern 104.
[0050]
FIG. 9 briefly described above is a diagram showing an example of an output time series pattern 104 obtained from the time series data 102 shown in FIG.
[0051]
Hereinafter, the processing operation by the above-described time series pattern generation unit 103 will be described in detail by taking as an example the case of generating the output time series pattern 104 of FIG. 9 from the time series data 102 of FIG.
[0052]
First, as shown in FIG. 3, the segmenting unit 301 segments the time-series data 102 (see FIG. 5) (see FIG. 6) to segment information 302 (length l1, height Δh) (length l2, Height Δh2)... Is generated and sent to the segment cutout unit 303.
[0053]
The segment cutout unit 303 uses the segment information 302 to generate segment data 305 (length 11, height Δh 1, shape y 1) (length 12, height Δh 2, shape y 2) (state data is not shown). )) Is generated and sent to the model group generation unit 306.
[0054]
The model group generation unit 306 uses the segment data 305 (length 11, height Δh 1, shape y 1) (length 12, height Δh 2, shape y 2) (state data not shown). A generated time-series pattern 307 (see FIG. 7A) consisting of time-similar patterns having a length of 1 and not similar to each other is generated and sent to the time-series pattern deleting unit 308.
[0055]
The time-series pattern deletion unit 308 deletes the time-series pattern having an appearance frequency of 1 or less from the received generated time-series pattern 307 and sends it to the continuous evaluation unit 311 as the remaining time-series pattern 309 after deletion (upper part of FIG. 9). reference).
[0056]
The continuous evaluation unit 311 determines the number of time-series patterns in the remaining time-series pattern 309 after deletion. In this case, since the number of patterns is not 0 (the number of patterns is 5 as shown in the upper part of FIG. 9), the post-deletion remaining time series pattern 309 is stored internally, and the post-deletion residual time series pattern 309 is continuously processed. To the unit 312.
[0057]
The continuation processing unit 312 generates segment data 305 having a pattern length of 2 using the received post-deletion remaining time-series pattern 309 and the time-series data 102 and sends the segment data 305 to the model group generation unit 306.
[0058]
Using the received segment data 305, the model group generation unit 306 generates a generation time series pattern 307 having a pattern length of 2 (see FIG. 7B) and sends it to the time series pattern deletion unit 308.
[0059]
The time series pattern deletion unit 308 deletes the time series pattern having an appearance frequency of 1 or less from the received generated time series pattern 307 (see the lower part of FIG. 9), and continues the evaluation unit 311 as the remaining time series pattern 309 after deletion. To send.
[0060]
The continuous evaluation unit 311 determines the number of time series patterns in the post-deletion remaining time series pattern 309. In this case, since the number of patterns is not 0 (the number of patterns is 2 as shown in the lower part of FIG. 9), the post-deletion remaining time series pattern 309 is stored, and the post-deletion remaining time series pattern 309 is stored in the continuation processing unit 312. Send it out.
[0061]
The continuation processing unit 312 generates segment data 305 with a pattern length of 3 using the received post-deletion remaining time-series pattern 309 and time-series data 102 and sends the segment data 305 to the model group generation unit 306.
[0062]
Using the received segment data 305, the model group generation unit 306 generates a generation time series pattern 307 having a pattern length of 3 (not shown) and sends it to the time series pattern deletion unit 308.
[0063]
The time-series pattern deleting unit 308 deletes the time-series pattern having an appearance frequency of 1 or less from the received generated time-series pattern 307 as a post-deletion remaining time-series pattern 309 (not shown) and sends it to the continuous evaluation unit 311. To do.
[0064]
The continuous evaluation unit 311 determines the number of time series patterns in the post-deletion remaining time series pattern 309. Since the number of time series patterns is 0 (not shown), the continuation evaluation unit 311 outputs the post-deletion remaining time series pattern 309 (see FIG. 9) having the previously stored pattern lengths 1 and 2 as the output time series pattern 104. Output as.
[0065]
Here, the model group generation unit 306 (see FIG. 3) constituting the time series pattern generation unit 103 will be described in more detail.
[0066]
FIG. 4 is a diagram illustrating a detailed configuration example of the model group generation unit 306.
[0067]
As described above, the model group generation unit 306 includes the segment data 305 (length 11, height Δh 1, shape y 1) (length 12, height Δh 2, shape y 2) (state data not shown). The generation time series pattern 307 is generated from the above.
[0068]
As shown in FIG. 4, the model group generation unit 306 includes a segment extraction continuation evaluation unit 401, a one segment extraction unit 402, a model likelihood calculation unit 404, a likelihood evaluation unit 405, a model relearning unit 406, and a new model. A creation unit 407 is provided.
[0069]
The segment extraction continuation evaluation unit 401 determines whether all the one segment data 403 in the segment data 305 (each data constituting the segment data 305 as described above) has been extracted by the next one segment extraction unit 402. . When the segment extraction continuation evaluation unit 401 determines that one segment data 403 cannot be extracted any more, that is, if all the one segment data 403 in the segment data 305 has been extracted, A series pattern (see FIGS. 7A and 7B) is taken out from a buffer (not shown) and output as a generation time series pattern 307.
[0070]
On the other hand, when the segment extraction continuation evaluation unit 401 determines that the one segment data 403 to be extracted still exists in the segment data 305, the process proceeds to the one segment extraction unit 402.
[0071]
The one segment extraction unit 402 extracts the one segment data 403 from the segment data 305 and sends it to the model likelihood calculation unit 404.
[0072]
The model likelihood calculation unit 404 is, for example, a likelihood function shown in the following (Equation 1).
[Expression 1]

Are used to calculate the likelihood (fitness) of the one segment data 403 for each time series pattern generation model 409 (time series pattern as an intermediate stage) to be described later.
[0073]
Here, in (Equation 1), l_i, Δh_i, Y_i(T), μ_li, Μ_Δhi, Σ_liAnd σ_ΔhiAre parameters of the time-series pattern generation model 409. More specifically, l_iIs the length, Δh_iIs height, y_i(T) shows shape data. Also, as will become clear from the following, μ_liAnd σ_liIs the mean and standard deviation of the length, μ_ΔhiAnd σ_ΔhiIndicates the mean and standard deviation of the height. On the other hand, l ’_i, Δh ’_i, Y ’_i(T) is a parameter of the one-segment data 403. G_{μ , σ}(X) represents a normal distribution function in which the average is μ and the standard deviation is σ.
[0074]
As shown in (Expression 1), this likelihood function obtains the degree of fit between the one-segment data 403 and each time-series pattern generation model from the viewpoints of length, height, and shape. Specifically, the likelihood function has three multiplication terms in Σ, the leftmost multiplication term is the fitness of length, the second multiplication term is the fitness of height, the third Corresponds to the conformity of the shape. The conformity of the shape is obtained by normalizing the shape data of the one-segment data 403 and the shape data of the time-series pattern generation model 409 (in a state in which the size is corrected to a reference size excluding the influence of the length and height). Seeking. Therefore, according to (Equation 1), even if the length and height are somewhat different, if the shapes are similar, the likelihood indicating high suitability is calculated. However, when the length or height of the one-segment data 403 is very different from the time-series pattern generation model 409, even if the shapes are similar, the length is low depending on the leftmost multiplication term and the second multiplication term. A likelihood indicating suitability is calculated.
[0075]
The model likelihood calculation unit 404 uses the maximum likelihood and the second likelihood of the likelihoods calculated using (Equation 1) as the likelihood data 408, as shown in FIG. It is sent to the next likelihood evaluation unit 405.
[0076]
The likelihood evaluating unit 405 is configured to calculate the maximum likelihood (maxQ calculated by the model likelihood calculating unit 404)._i) And second likelihood (secondQ_i) Based on the likelihood ratio calculation formula shown in the following (Formula 2)
[Expression 2]

The likelihood ratio R calculated by is calculated.
[0077]
The likelihood evaluation unit 405 calculates the calculated likelihood ratio R = maxQ_i/ SecondQ_iIs compared with the input similarity parameter 106 (a determination reference value for determining the similarity between the one-segment data 403 and the time-series pattern generation model). Likelihood evaluation section 405 uses likelihood ratio R = maxQ._i/ SecondQ_iIs determined to be equal to or less than the similarity parameter 106, it is determined that there is no time-series pattern generation model 409 similar to the one-segment data, and the one-segment data 403 is sent to the new model creation unit 407.
[0078]
The new model creation unit 407 receives the length, height and shape data of the received one-segment data 403, and the average of the length and height (here, the length and height of the one-segment data are used as initial values) A time-series pattern generation model 409 is created which includes standard deviations of length and height (here, 0 as an initial value or 30% of the average of standard deviations already obtained). The new model creation unit 407 stores the created time-series pattern generation model 409 in a buffer (not shown), and sends the continuation instruction data 412 to the segment extraction continuation evaluation unit 401, and shifts the processing to the segment continuation evaluation unit 401. .
[0079]
On the other hand, the likelihood evaluation unit 405 calculates the calculated likelihood ratio R = maxQ._i/ SecondQ_iIs determined to be larger than the similarity parameter 106, the one-segment data 403 has a maximum likelihood maxQ_iIs determined to be similar to the time-series pattern generation model 409. Likelihood evaluation unit 405 sends 1-segment data 403 and similar information 410 (information indicating which time-series pattern generation model is similar) to model re-learning unit 406, to model re-learning unit 406. Migrate processing.
[0080]
The model re-learning unit 406 takes out the time-series pattern generation model specified in the similar information 410 from a buffer (not shown), and sets the parameters of this time-series pattern generation model according to the average / standard deviation update function shown in (Equation 3) below. Learn (update).
[Equation 3]

[0081]
As shown in (Expression 3), the model re-learning unit 406 does not learn the shape, and updates only the average length and standard deviation, and the average height and standard deviation. The model relearning unit 406 uses the updated time series pattern generation model 409 to replace the corresponding time series pattern generation model 409 in a buffer (not shown). On the other hand, the model re-learning unit 406 sends the continuation instruction data 412 to the segment extraction continuation evaluation unit 401 and shifts the processing to the segment extraction continuation evaluation unit 401.
[0082]
Hereinafter, the processing operation by the model group generation unit 306 will be described by taking as an example a case where a generation time series pattern 307 (see FIG. 7A) having a segment length of 1 is generated.
[0083]
First, as shown in FIG. 4, the segment extraction continuation evaluation unit 401 determines whether or not one segment data 403 to be extracted exists in the segment data 305 (the one at the time of walking). Here, the segment extraction continuation evaluation unit 401 determines that there is one segment data 403 to be extracted, and shifts the processing to the one segment extraction unit 402.
[0084]
The 1-segment extraction unit 402 extracts 1-segment data 403 (see the section of the shape y1 in FIG. 6) from the segment data 305 and sends it to the model likelihood calculation unit 404.
[0085]
Since the model likelihood calculation unit 404 does not yet have the time series pattern generation model 409 in the buffer (not shown) (at least two time series pattern generation models are required to calculate the likelihood ratio R), the likelihood is calculated. Without calculation, the received one-segment data 403 is sent to the likelihood evaluation unit 405 as it is.
[0086]
The likelihood evaluating unit 405 also sends the received one segment data 403 to the new model creating unit 407 as it is.
[0087]
The new model creation unit 407 uses the received 1-segment data 403 to generate shape data, length and height, and average and standard deviation of the length and height as the first time-series pattern generation model 409. And stored in a buffer (not shown). Also, the new model creation unit 407 sends continuation instruction data 412 to the segment extraction continuation evaluation unit 401.
[0088]
The segment extraction continuation evaluation unit 401 that has received the continuation instruction data 412 determines whether there is still one segment data 403 to be extracted in the segment data 305 (see FIG. 6). Here, the segment extraction continuation evaluation unit 401 determines that there is still one segment data 403 to be extracted, and shifts the processing to the one segment extraction unit 402.
[0089]
The 1-segment extraction unit 402 extracts the next 1-segment data 403 (see the section of the shape y2 in FIG. 6) from the segment data 305 and sends it to the model likelihood calculation unit 404.
[0090]
The model likelihood calculation unit 404 sends the one segment data 403 to the likelihood evaluation unit 405 as it is, as described above.
[0091]
The likelihood evaluating unit 405 also sends the received one segment data 403 to the new model creating unit 407 as it is.
[0092]
The new model creation unit 407 generates a second time-series pattern generation model 409 using the received one segment 403 in the same manner as described above, stores it in a buffer (not shown), and extracts the segment extraction continuation evaluation unit. The continuation instruction data 412 is sent to 401.
[0093]
The segment extraction continuation evaluation unit 401 that has received the continuation instruction data 412 determines whether there is still one segment data to be extracted in the segment data 305 (see FIG. 6). Here, the segment extraction continuation evaluation unit 401 determines that there is still one segment data 403 to be extracted, and shifts the processing to the one segment extraction unit 402.
[0094]
The 1-segment extraction unit 402 further extracts the next 1-segment data 403 (see the section of the shape y3 in FIG. 6) from the segment data 305 and sends it to the model likelihood calculation unit 404.
[0095]
The model likelihood calculating unit 404 inputs the first time-series pattern generation model 409 and the one-segment data in the above-described buffer (not shown) to the likelihood function (Equation 1), and inputs the first time-series pattern generation model. The likelihood for 409 is obtained. Further, the model likelihood calculating unit 404 inputs the above-described second time series pattern generation model 409 and one segment data into the likelihood function (Equation 1), and the likelihood for the second time series pattern generation model 409. Ask for. The model likelihood calculating unit 404 calculates the likelihood (likelihood data 408) for the calculated first and second time series pattern generation models 409 and the one segment data 403 received from the one segment extracting unit 402 as the likelihood. The data is sent to the evaluation unit 405.
[0096]
The likelihood evaluation unit 405 calculates a likelihood ratio R using the likelihood data 408 according to (Equation 2), and compares the calculated likelihood ratio R with the input similarity parameter 106. Here, the likelihood evaluation unit 405 determines that the likelihood ratio R is equal to or less than the similarity parameter 106 (determines that it is not similar to any of the first and second time-series pattern generation models 409). 1 segment data 403 is sent to the new model creation unit 407.
[0097]
The new model creation unit 407 generates a third time series model generation model 409 using the received 1-segment data 403 and stores it in a buffer (not shown) in the same manner as described above. The continuation instruction data 412 is sent to 401.
[0098]
The segment extraction continuation evaluation unit 401 that has received the continuation instruction data 412 determines whether there is still one segment data 403 to be extracted in the segment data 305 (see FIG. 6). Here, the segment extraction continuation evaluation unit 401 determines that there is still one segment data 403 to be extracted, and shifts the processing to the one segment extraction unit 402.
[0099]
The 1-segment extraction unit 402 further extracts the next 1-segment data 403 (see the section of the shape y4 in FIG. 6) from the segment data 305 and sends it to the model likelihood calculation unit 404.
[0100]
The model likelihood calculation unit 404 calculates the likelihood of the one segment data 403 for the first, second, and third time series pattern generation models using the likelihood function (Equation 1), as described above. The model likelihood calculating unit 404 evaluates the likelihood of the first and second largest (likelihood data 408) of the calculated three likelihoods and the one segment data 403 received from the one segment extracting unit 402. To the unit 405.
[0101]
The likelihood evaluating unit 405 calculates a likelihood ratio R according to (Equation 2) using the received likelihood data 408. The likelihood evaluation unit 405 compares the calculated likelihood ratio R with the similarity parameter 106. Here, the likelihood evaluation unit 405 determines that the likelihood ratio R is larger than the similarity parameter 106. The likelihood evaluation unit 405 determines that the time series pattern generation model having the maximum likelihood (here, the first time series pattern generation model) is similar to the one segment data 403. Therefore, the likelihood evaluation unit 405 sends the one-segment data 403 and the similar information 410 to the model relearning unit 406.
[0102]
The model re-learning unit 406 identifies the first time-series pattern generation model 409 based on the similar information 410, and uses the first time-series pattern generation model 409 and the one-segment data 403, to According to 3), the parameters of the first time-series pattern generation model 409 are learned (updated). As described above, shape learning is not performed, and only the average of length and height and the standard deviation are updated. The model relearning unit 406 that has updated the parameters sends the continuation instruction data 412 to the segment extraction continuation evaluation unit 401.
[0103]
The processing described above is performed by using the remaining one segment data 403 in the segment data 305 (refer to the section of the shape y5 in FIG. 6) and the one segment data 403 at the time of traveling (refer to the sections of the shapes y6 to y10 in FIG. 6). ), And finally, five time-series pattern generation models 409 (see FIG. 7A) having a pattern length of 1 are acquired (stored in a buffer (not shown)). When the segment extraction continuation evaluation unit 401 determines that there is no one segment data to be extracted from the segment data 305, the five time-series pattern generation models 409 acquired above are determined as time-series patterns, respectively. Is generated as a generated time series pattern 307 (see FIG. 7A). As can be seen from the above, each time-series pattern constituting the generated time-series pattern 307 includes the length, height, and shape of the one-segment data from which the time-series pattern generation model is newly generated, With mean and standard deviation of length and height. The length l, the height Δh, and the shape y given to each time series pattern shown in FIGS. 7, 8, 9, 11, and 12 are the length of this one segment data, Height and shape.
[0104]
The processing operation of the model group generation unit 306 when the pattern length is 1 has been described above, but the same operation is performed when the pattern length is 2, 3,.
[0105]
Next, as shown in FIG. 1, the time series pattern evaluation unit 105 will be described.
[0106]
FIG. 2 is a diagram showing in detail the configuration of the time series pattern evaluation unit 105.
[0107]
As described above, the time series pattern evaluation unit 105 determines whether or not the output time series pattern 104 satisfies a desired criterion, and if so, outputs the final time series pattern 107 as described later, If not satisfied, the similarity parameter 106 is corrected.
[0108]
As illustrated in FIG. 2, the time series pattern evaluation unit 105 includes an appearance probability calculation unit 201, a time series pattern evaluation value calculation unit 203, a time series pattern evaluation value evaluation unit 205, and a similarity parameter correction unit 206.
[0109]
The appearance probability calculation unit 201 examines the frequency (appearance frequency) at which each time series pattern (see FIG. 9) constituting the output time series pattern 104 appears in each time series state (for example, walking and running). Then, the appearance probability calculation unit 201 calculates the probability (appearance probability) that each time series pattern appears in each state based on the result of the examination, and sets the calculation result as the appearance probability 202 to calculate the time series pattern evaluation value To 203.
[0110]
FIG. 10 shows the appearance frequency (walking frequency and running frequency) in each state calculated for each time series pattern 1 to 5, 12, 45 (output time series pattern 104) in FIG. 10 is a chart showing a walking probability and a running probability) and a time-series pattern evaluation value 204 (a set of time-series pattern individual evaluation values).
[0111]
As shown in FIG. 10, it can be seen whether each time-series pattern 1-5, 12, 45 (see FIG. 9) appears frequently during walking or running. For example, the time series pattern 1 has a walking frequency of 2 and a running frequency of 0, and thus the walking probability is 1 and the running probability is 0.
[0112]
The time series pattern evaluation value calculation unit 203 uses the appearance probability 202 received from the appearance probability calculation unit 201 to calculate each time series pattern individual evaluation value for evaluating each time series pattern, and calculates the calculated time series pattern. The individual evaluation value is sent to the time series pattern evaluation value evaluation unit 205 as the time series pattern evaluation value 204. Here, the time-series pattern individual evaluation value is a numerical representation of the state of bias of each time-series pattern to each state.
[0113]
Each time series pattern individual evaluation value shown in FIG. 10 is calculated based on the average information amount (entropy) of each time series pattern. In this evaluation, each time series pattern becomes a good time series pattern that is biased to each state as the value of the average information amount is lower. For example, the patterns 1, 2, 4, 5, 12, and 45 having the time series pattern individual evaluation value of 0 can be said to be preferable time series patterns that appear only when walking or running. On the other hand, the pattern 3 having the time series pattern individual evaluation value of 0.150515 appears at the same probability during walking and running, and can be said to be an unfavorable time series pattern.
[0114]
The time series pattern evaluation value evaluation unit 205 evaluates the output time series pattern 104 using the time series pattern evaluation value 204 received from the time series pattern evaluation value calculation unit 203 and the input output time series pattern 104. .
[0115]
More specifically, the time series pattern evaluation value evaluation unit 205 uses the time series pattern evaluation value 204 to calculate a time series pattern overall evaluation value for evaluating whether or not the entire output time series pattern 104 satisfies a desired criterion. To do.
[0116]
The time series pattern evaluation value evaluation unit 205 removes the time series pattern in which the time series pattern individual evaluation value is outside the predetermined reference if the calculated time series pattern overall evaluation value is within the predetermined reference. Output as a sequence pattern 107.
[0117]
On the other hand, the time series pattern evaluation value evaluation unit 205 sends the time series pattern evaluation value 204 to the similarity parameter correction unit 206 if the calculated time series pattern overall evaluation value is outside a predetermined reference.
[0118]
Hereinafter, the evaluation of the output time series pattern 104 will be specifically described.
[0119]
First, the case where the entire time series pattern evaluation value falls within a predetermined standard will be described with reference to FIGS.
[0120]
First, the time-series pattern evaluation value evaluation unit 205 calculates an average of the time-series pattern evaluation values 204 and sets this as the time-series pattern overall evaluation value. The time-series pattern evaluation value evaluation unit 205 calculates the calculated time-series pattern overall evaluation value and the overall setting allowable value 0.11 set by the user or the like (shows the level of the output time-series pattern 104 that is satisfactory to the user, For example, 0.15 or less). That is, the time-series pattern evaluation value evaluation unit 205 compares the average value 0.022 (= 0.150515 / 7) of the time-series pattern evaluation value 204 of FIG. 10 with the overall setting allowable value 0.11. Since the average value 0.022 is smaller than the overall setting allowable value 0.11, the time series pattern evaluation value evaluation unit 205 determines that the time series pattern overall evaluation value is within a predetermined standard. The time-series pattern evaluation value evaluation unit 205 is an individual setting allowable value (at a level for evaluating each time-series pattern that is satisfactory to the user among the time-series patterns constituting the output time-series pattern 104 (see FIG. 9). Yes, here, it is determined whether or not there is a time-series pattern (see FIG. 10) having a time-series pattern individual evaluation value higher than that having the same value 0.11 as the overall setting allowable value. When the time-series pattern evaluation value evaluating unit 205 determines that such a time-series pattern exists, the time-series pattern (the time-series pattern in FIG. 10) can be understood by comparing FIG. 9 and FIG. 3) is removed from the output time series pattern 104. Then, the time series pattern evaluation value evaluation unit 205 outputs the output time series pattern 104 after removal (see FIG. 11) as the final time series pattern 107 (see FIG. 2).
[0121]
In the above description, the same value is used as the overall setting allowable value and the individual setting allowable value, but different values may be set.
[0122]
Next, a case where the time series pattern overall evaluation value calculated from the time series pattern evaluation value 204 is outside a predetermined standard will be described with reference to FIGS. 12 and 13.
[0123]
FIG. 12 is a diagram illustrating an example of the output time series pattern 104 generated in the time series pattern generation unit 103 (see FIG. 1) by the similarity parameter 106 having a value different from the above (a value looser than the above). This output time series pattern 104 is generated from the time series data 102 of FIG. 5 as described above.
[0124]
As shown in FIG. 12, the time series patterns 12 and 45 (see FIG. 9) recognized as different time series patterns in FIG. 9 are recognized as the same time series pattern 12 here. Similarly, time series patterns 1 and 4 recognized as different time series patterns in FIG. 9 are the same pattern 1 here, and time series patterns 2 and 2 recognized as different time series patterns in FIG. 5 are recognized as the same time-series pattern 2 here.
[0125]
FIG. 13 is a chart showing the appearance frequency in each state, the appearance probability 202 in each state, and the time series pattern evaluation value 204 calculated in the same manner as described above for each time series pattern in FIG.
[0126]
Here, the evaluation of the output time series pattern 104 (see FIG. 12) will be described as follows.
[0127]
Similarly to the above, first, the time series pattern evaluation value evaluation unit 205 (see FIG. 2) calculates the average of the time series pattern evaluation values 204 (time series pattern overall evaluation value) and the overall setting allowable value 0.11 (predetermined value). The standard). As can be seen from FIG. 13, the average of the time series pattern evaluation values 204 (time series pattern overall evaluation value) is 0.150515, which is larger than the overall setting allowable value 0.11. Therefore, the time-series pattern evaluation value evaluation unit 205 determines that the overall time-series pattern evaluation value is outside a predetermined standard. That is, the time series pattern evaluation value evaluation unit 205 determines that the output time series pattern 104 in FIG. 12 does not satisfy the purpose, and converts the time series pattern evaluation value 204 to the similarity parameter correction unit as shown in FIG. To 206.
[0128]
The similarity parameter correction unit 206 corrects the similarity parameter 206 value (makes it stricter) using the time series pattern evaluation value 204 received from the time series pattern evaluation value evaluation unit 205. As an example of correction, the ratio of the difference between the average of the time-series pattern evaluation values 204 (the initial value is 0, for example) and the difference between the previous time of the similarity parameter 106 (the initial value is 0) Is multiplied by a constant, and this is subtracted from the similarity parameter 106 of this time.
[0129]
The similarity parameter correction unit 206 sends the corrected similarity parameter 106 calculated as described above to the time-series pattern generation unit 103 as shown in FIG.
[0130]
As described above, according to the present embodiment, the time length of the time series patterns that are not similar to each other as much as indicated by the similarity parameter is fixed from the time series data, including those having complicated shapes. Can be generated without.
[0131]
In addition to the time-series data, the time-series state is also used, so that it is possible to generate a time-series pattern that does not appear in other states as much as possible.
[0132]
Further, the similarity parameter can be corrected any number of times until a time series pattern that meets the target standard is obtained, and finally, a unique time series pattern can be obtained for all the time series states. At this time, it is possible to generate a time series pattern according to a target criterion earlier by efficiently correcting the similar parameters.
[0133]
Moreover, since the appearance probability in each state of the generated time series pattern is calculated, for example, an average information amount can be used as the time series pattern individual evaluation value.
[0134]
Further, since a time series pattern with a low appearance frequency is deleted, an effective time series pattern can be generated.
[0135]
In addition, segment data is extracted after the pattern length has been increased so that time series data corresponding to each time series pattern in the remaining time series pattern after deletion is included, so segmentation of less important segment data is ignored. Thus, efficient segment data can be extracted.
[0136]
In addition, not only segment information (length and height) but also the normalized shape is used to calculate the likelihood for the time-series pattern generation model, so the shape of the segment data retains a complex shape Even if this is done, it is possible to determine the appropriate similarity. That is, even if the segment data has a complicated shape, time series patterns that are not similar to each other can be efficiently generated from the time series data by absorbing the ambiguity of length and height.
[0137]
In addition, it is evaluated whether or not the obtained time series pattern meets the purpose, and the time series pattern that does not meet the purpose is removed, so that only the time series pattern that meets the purpose can be reliably extracted.
[0138]
Further, since a time series pattern biased to each time series state can be extracted, class classification based on the time series pattern can also be performed.
[0139]
In addition, since the time series pattern overall evaluation value (average of time series pattern individual evaluation values) is compared with the overall set allowable value (predetermined standard), the output time series pattern is evaluated, which is easy for the user to understand. It is possible to set a threshold value.
[0140]
In addition, by displaying the appearance probability of the time series pattern in each state, the entire time series pattern evaluation value, the time series pattern individual evaluation value, and the like on the screen or the like, the user can select the time series pattern himself.
[0141]
【The invention's effect】
According to the present invention, a unique time series pattern that can be distinguished from other states for each state can be automatically generated based on time series data obtained from each state.
[Brief description of the drawings]
FIG. 1 is a diagram illustrating a configuration example of a time-series pattern generation apparatus according to an embodiment of the present invention.
FIG. 2 is a diagram illustrating a configuration example of a time series pattern evaluation value evaluation unit in the time series pattern generation device.
FIG. 3 is a diagram illustrating a configuration example of a time series pattern generation unit in the time series pattern generation apparatus.
FIG. 4 is a diagram illustrating a configuration example of a model group generation unit in a time series pattern generation unit.
FIG. 5 is a diagram for explaining state data and time-series data.
FIG. 6 is a diagram for explaining segment information, segment data, and one-segment data.
FIG. 7 is a diagram showing an example of a generated time series pattern.
FIG. 8 is a diagram showing an example of a time series pattern remaining after deletion (when the pattern length is 2);
FIG. 9 is a diagram showing an example of an output time series pattern.
FIG. 10 is a chart showing an example of appearance frequency, appearance probability, and time-series pattern individual evaluation value.
FIG. 11 is a diagram showing an example of a final time series pattern.
FIG. 12 is a diagram showing an example of an output time-series pattern at an intermediate stage.
FIG. 13 is a chart showing an example of an appearance probability and a time-series pattern individual evaluation value at an intermediate stage.
[Explanation of symbols]
101 Status data
102 Time series data
103 Time-series pattern generator
104 Output time series pattern
105 Time-series pattern evaluation unit
106 Similarity parameter
107 Final time series pattern
201 Appearance probability calculator
202 Appearance probability
203 Time series pattern evaluation value calculator
204 Time series pattern evaluation value
205 Time-series pattern evaluation value evaluation unit
206 Similarity parameter correction unit
301 Segmentation part
302 Cement information
303 Segment cutout
305 segment data
306 Model group generator
307 Generation time series pattern
308 Time series pattern deletion part
309 Time series pattern remaining after deletion
311 Continuous Evaluation Department
312 Continuation processing section
401 Segment extraction continuation evaluation department
402 1 segment take-out part
403 1 segment data
404 Model likelihood calculator
405 Likelihood evaluation unit
406 Model re-learning section
407 New model creation department

Claims (25)

A time series pattern generation device that generates time series patterns specific to each state using time series data corresponding to a plurality of states to be observed,
A plurality of pieces of partial data are cut out from the plurality of pieces of time series data, and a plurality of pieces of partial data having the same similarity are used from the plurality of pieces of partial data by using a similarity determination criterion for determining the degree of similarity between the pieces of partial data. Extracting a partial data group and generating a time series pattern corresponding to each of the plurality of partial data groups; and
Evaluate whether or not the time series pattern generated by the time series pattern generation unit conforms to a predetermined standard, and if within the predetermined standard, each of the time series pattern and each of the time series pattern Based on the relationship with the appearance frequency in each of the time-series data, the time-series pattern is identified as one of the states, and the identified time-series pattern is output as a final time-series pattern, If it is not a predetermined criterion, the similarity determination criterion is corrected, and the time series pattern generation unit is corrected to generate the time series pattern again using the corrected similarity determination criterion. A time series pattern evaluation unit that outputs a similarity determination criterion to the time series pattern generation unit;
A time-series pattern generation device comprising: The time-series pattern evaluation unit
An appearance probability calculating unit for calculating a probability that each time series pattern appears in each time series data;
A time-series pattern evaluation value calculation unit that calculates a time-series pattern overall evaluation value for evaluating the entire time-series pattern based on the calculated appearance probability;
The calculated time-series pattern overall evaluation value is compared with an overall setting allowable value, and when the time-series pattern overall evaluation value is within the criteria based on the overall setting allowable value, the appearance probability of the time-series pattern is the highest. A time series pattern evaluation value evaluation unit that identifies each time series pattern in the high state,
When the calculated time-series pattern overall evaluation value is outside the reference based on the overall setting allowable value, the time-series pattern generation unit corrects the similarity determination criterion using the time-series pattern overall evaluation value. Again, a similarity determination reference correction unit that generates the time series pattern using the corrected similarity determination reference,
The time series pattern generation device according to claim 1, comprising: The time series pattern evaluation value calculation unit calculates a time series pattern individual evaluation value for evaluating the time series pattern for each time series pattern based on the calculated appearance probability,
The time series pattern evaluation value evaluation unit compares the time series pattern individual evaluation value with an individual setting allowable value when the time series pattern overall evaluation value is within a standard based on the overall setting allowable value, and the time series If the pattern individual evaluation value is outside the reference by the individual setting allowable value, the time series pattern corresponding to the time series pattern individual evaluation value outside the reference is removed,
The similarity determination reference correction unit, when the overall evaluation value of the time series pattern is outside the reference based on the overall setting allowable value, at least one of the individual evaluation value of the time series pattern and the overall evaluation value of the time series pattern To correct the similarity criterion,
The time-series pattern generation apparatus according to claim 2, wherein: The time-series pattern generation unit
A partial data cutout unit that divides the time series data into several segments according to a certain standard, and cuts out a plurality of the partial data from each of the time series data in a specific segment length unit;
Extracting the plurality of partial data groups having the same similarity from the plurality of partial data according to the similarity determination criterion, and including the number of partial data included in the partial data group as an appearance frequency A model group generation unit that generates the time-series pattern corresponding to each of the partial data groups;
A time series pattern deletion unit that deletes the time series pattern having the appearance frequency equal to or lower than a predetermined deletion threshold from the time series pattern generated by the model group generation unit;
The number of the time series patterns remaining after the deletion is evaluated to make a continuation or non-continuation determination. In the case of the continuation, the time series patterns remaining after the deletion are stored, and the segment data extraction unit stores the segment. The partial data is cut out by changing the length, while in the case of non-continuation, the time series pattern for each of the stored segment lengths is sent to the time series pattern evaluation section,
The time series pattern generation device according to claim 1, comprising: The partial data cutout unit includes, in the partial data cutout after the segment length change, including the partial data corresponding to the time series pattern remaining after the previous deletion in a part of the partial data cut out. The time-series pattern generation device according to claim 4, wherein The model group generation unit includes a new model creation unit that creates a time series pattern generation model that is a model for generating the time series pattern, and a model re-learning unit that corrects the time series pattern generation model,
Further, the partial data is extracted from a plurality of the partial data, the likelihood for each time-series pattern generation model of the extracted partial data, the length and the height by the normalization of the partial data A model likelihood calculation unit for calculating from the viewpoint of the shape excluding the influence of height,
Using the likelihood calculated for each time series pattern generation model and the similarity parameter as the similarity determination criterion for determining the similarity between the partial data and the time series pattern generation model, A likelihood evaluation unit that evaluates whether or not the time series pattern generation model similar to the extracted partial data exists, and when the similar time series pattern generation model does not exist, the new model When the time series pattern generation model similar to the time series pattern generation model exists using the partial data in the creation unit, the time series pattern generation model having the highest likelihood is A likelihood evaluation unit that causes the model re-learning unit to correct the partial data using the partial data;
The model likelihood calculation unit determines whether to continue the process of extracting the partial data from the plurality of partial data. If not, the time series pattern generation model is determined as the time series pattern. A continuation processing unit that outputs to the time series pattern deletion unit,
The time-series pattern generation device according to claim 4, comprising: The likelihood evaluation unit compares the similarity parameter with a ratio between the highest likelihood and the next highest likelihood among the likelihoods calculated for each time series pattern generation model. The time-series pattern generation apparatus according to claim 6, wherein the time-series pattern generation model similar to the partial data is evaluated for existence. The new model creation unit calculates the average and standard deviation of the length and height of the partial data as a part of the time-series pattern generation model to be created,
The model relearning unit corrects the average and standard deviation of the length and height possessed by the time-series pattern generation model,
The model likelihood calculation unit uses the average and standard deviation of the length and height held by each time series pattern generation model in calculating the likelihood for each time series pattern generation model, The time series pattern generation device according to claim 6 or 7. A time series pattern generation method for generating a time series pattern specific to each state using time series data corresponding to a plurality of states to be observed,
A plurality of pieces of partial data are cut out from the plurality of pieces of time series data, and a plurality of pieces of partial data having the same similarity are used from the plurality of pieces of partial data by using a similarity determination criterion for determining the degree of similarity between the pieces of partial data. Extracting a partial data group and generating a time series pattern corresponding to each of the plurality of partial data groups; and
Evaluate whether the time series pattern generated by the time series pattern generation step conforms to a predetermined standard, and if within the predetermined standard, each of the time series pattern and each of the time series pattern Based on the relationship with the appearance frequency in each of the time-series data, the time-series pattern is identified as one of the states, and the identified time-series pattern is output as a final time-series pattern, If it is not a predetermined criterion, the similarity determination criterion is corrected, and the time series pattern generation step is performed again according to the corrected similarity determination criterion.
A time-series pattern generation method comprising: The time series pattern evaluation step includes:
An appearance probability calculating step for calculating a probability that each time series pattern appears in each of the time series data;
A time-series pattern evaluation value calculating step for calculating a time-series pattern overall evaluation value for evaluating the entire time-series pattern based on the calculated appearance probability;
The calculated time-series pattern overall evaluation value is compared with an overall setting allowable value, and when the time-series pattern overall evaluation value is within the criteria based on the overall setting allowable value, the appearance probability of the time-series pattern is the highest. A time series pattern evaluation value evaluation step for identifying each time series pattern in the high state and outputting the identified time series pattern as the final time series pattern;
When the calculated time-series pattern overall evaluation value is outside the reference based on the overall setting allowable value, the similarity determination criterion is corrected using the time-series pattern overall evaluation value, and the corrected similarity Similarity determination criterion correction step for performing the time series pattern generation step again according to the determination criterion;
The time series pattern generation method according to claim 9, further comprising: In the time series pattern evaluation value evaluation step, the time series pattern overall evaluation value is not associated with the time series pattern among a plurality of the states even if the time series pattern overall evaluation value is within a standard based on the overall setting allowable value. 11. The time series pattern generation method according to claim 10, wherein if it is determined that a state exists, the similarity determination reference correction step is executed again without outputting the final time series pattern. The time series pattern evaluation value calculation step calculates a time series pattern individual evaluation value for evaluating the time series pattern for each time series pattern based on the calculated appearance probability,
The time series pattern evaluation value evaluation step compares the time series pattern individual evaluation value with an individual setting allowable value when the time series pattern overall evaluation value is within a standard based on the overall setting allowable value, and the time series If the pattern individual evaluation value is outside the reference by the individual setting allowable value, the time series pattern corresponding to the time series pattern individual evaluation value outside the reference is removed,
In the similarity determination reference correction step, when the time series pattern overall evaluation value is outside the reference based on the overall setting allowable value, at least one of the time series pattern individual evaluation value and the time series pattern overall evaluation value is determined. To correct the similarity criterion,
The time-series pattern generation method according to claim 10 or 11, characterized in that A time series pattern generation method for generating time series patterns that are not similar to each other from time series data,
A partial data cutting step of dividing the time series data into a number of segments on a certain basis, and cutting out a plurality of the partial data from each of the time series data in a specific segment length unit;
A plurality of partial data groups having the same similarity relationship are extracted from the plurality of partial data using a similarity determination criterion for determining the similarity between the partial data, and the portions included in the partial data group A model group generation step for generating the time series pattern corresponding to each of the plurality of partial data groups in a state including the number of data as the appearance frequency;
A time series pattern deletion step of deleting the time series pattern having the appearance frequency equal to or lower than a predetermined deletion threshold from the time series pattern generated by the model group generation step;
The number of the time series patterns remaining after the deletion is evaluated to make a continuation or non-continuation determination.In the case of continuation, the time series patterns remaining after the deletion are stored, and the segment length is changed to change each of the time series patterns. On the other hand, in the case of non-continuation, a continuous evaluation step of outputting the time series pattern for each of the stored segment lengths,
A time-series pattern generating method comprising: The partial data extraction step includes including the partial data corresponding to the time-series pattern remaining after the previous deletion in the partial data to be extracted in the partial data extraction after the segment length change. The time-series pattern generation method according to claim 13, wherein The time series pattern generation method according to claim 9, wherein the time series pattern generation step uses the time series pattern generation method according to claim 13 or 14. A time series pattern generation method for generating time series patterns that are not similar to each other using a plurality of partial data extracted from time series data,
A new model creation step of creating a time series pattern generation model to be a model for generating the time series pattern, and a model relearning step of correcting the time series pattern generation model,
Model likelihood for calculating the likelihood of each partial data for each time-series pattern generation model from the viewpoint of the length and height of the partial data and the shape excluding the influence of the length and the height by normalization. A degree calculation step;
Using the likelihood calculated for each time series pattern generation model and a similarity parameter as a similarity determination criterion for determining the similarity between the partial data and the time series pattern generation model A likelihood evaluation step for evaluating whether or not the time series pattern generation model similar to the partial data exists, and when the similar time series pattern generation model does not exist, the new model creation step To create the time series pattern generation model, and when there is a similar time series pattern generation model, the time series pattern generation model having the highest likelihood is subjected to the model relearning step. A likelihood evaluation step to correct;
It is determined whether to repeat the model likelihood calculation step and the likelihood evaluation step for the other partial data, and if it is determined to repeat, the model likelihood calculation step and the likelihood evaluation step When it is determined that the partial data is not repeated, the continuous processing step of determining each time series pattern generation model in the final state as each time series pattern;
A time-series pattern generating method comprising: The likelihood evaluation step compares the ratio of the highest likelihood to the next highest likelihood among the likelihoods calculated for each time-series pattern generation model with the similarity parameter, The time series pattern generation method according to claim 16, wherein an evaluation is made as to whether or not the time series pattern generation model similar to the partial data exists. The new model creation step calculates the average and standard deviation of the length and height of the partial data as a part of the time-series pattern generation model to be created,
The model relearning step corrects the average and standard deviation of the length and height possessed by the time series pattern generation model,
The model likelihood calculating step uses the average and standard deviation of the length and height possessed by each time series pattern generation model in the calculation of the likelihood for each time series pattern generation model. The time series pattern generation method according to claim 16 or 17. The time series pattern generation method according to claim 13, wherein the model group generation step uses the time series pattern generation method according to claim 16. The time series pattern generation method according to claim 9, wherein the time series pattern generation step uses the time series pattern generation method according to any one of claims 16 to 18 in generating the time series pattern. . In the time series pattern evaluation step, the ratio of the difference between the average of the time series pattern individual evaluation values and the previous time of the average and the difference of the similarity parameter is multiplied by a constant, and is subtracted from the current similarity parameter. 21. The time series pattern generation method according to claim 20, wherein the similarity parameter is corrected. The time-series pattern evaluation value calculating step represents the appearance probability in each time-series data of the time-series pattern as a maximum probability and a sum of other probabilities, and the maximum probability and the sum of the probabilities The average information amount of the time series pattern is calculated using the time series pattern, and the overall evaluation value of the time series pattern is calculated using the average information amount calculated for each of the time series patterns. A time-series pattern generation method described in 1. The time-series pattern evaluation value calculating step represents the appearance probability in each time-series data of the time-series pattern as a maximum probability and a sum of other probabilities, and the maximum probability and the sum of the probabilities 13. The time series pattern generation method according to claim 12, wherein an average information amount of the time series pattern is calculated by using the average information amount as the time series pattern individual evaluation value. 24. The time series pattern generation method according to claim 12, wherein the time series pattern evaluation value calculation step uses an average of the time series pattern individual evaluation values as the whole time series pattern evaluation value. The appearance probability calculated in the appearance probability calculation step, the entire time series pattern evaluation value calculated in the time series pattern evaluation value calculation step, and the time series pattern calculated in the time series pattern evaluation value calculation step The time series pattern generation method according to any one of claims 12, 22 to 24, further comprising a step of outputting at least one of the individual evaluation values to an output unit.

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