CN102111284A - Method and device for predicting telecom traffic - Google Patents

Method and device for predicting telecom traffic Download PDF

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Publication number
CN102111284A
CN102111284A CN200910243569XA CN200910243569A CN102111284A CN 102111284 A CN102111284 A CN 102111284A CN 200910243569X A CN200910243569X A CN 200910243569XA CN 200910243569 A CN200910243569 A CN 200910243569A CN 102111284 A CN102111284 A CN 102111284A
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carrying capacity
traffic carrying
prediction
traffic
sample
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CN102111284B (en
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章建功
潘阳发
满毅
张海军
陈晓峰
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Beijing BOCO Inter-Telecom Technology Co., Ltd.
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Bright Oceans Inter Telecom Co Ltd
Bright Oceans Inter Telecom Software Research Institute Co Ltd
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Abstract

The invention provides a method and device for predicting telecom traffic. The method comprises the following steps: determining the prediction granulation of the telecom traffic; selecting a historical sample and a prediction sample; respectively calculating the growth rates and the initial values of the telecom traffics of the historical sample and the prediction sample by using a unary linear regression model; calculating the prediction traffics of the historical sample and the prediction sample; reading the actual traffic of the historical sample; and adjusting the second prediction traffic according to the deviation between the traffic of first prediction and the actual traffic to obtain the prediction traffic adjusted by a user. The behavior mode of the user is obtained through analyzing the deviation between the linear prediction value and the actual value in the historical sample, and accordingly, the linear prediction value of future telecom traffic data is adjusted to obtain more accurate telecom network traffic, thereby providing decision support for the user to plan, organize and manage network more accurately.

Description

Telecommunication traffic Forecasting Methodology and device
Technical field
The present invention relates to the telecommunication network technology field, particularly relate to a kind of telecommunication traffic Forecasting Methodology and device.
Background technology
Telecommunication traffic is the telecom information quantity that expression needs transmission, is to adjust the main foundation that all kinds of network element devices, producers and tissue are produced, for example telephone traffic, multimedia message amount, note amount etc.The characteristics of telecommunication service are that traffic carrying capacity changes greatly, and are busy not busy uneven, influenced significantly by festivals or holidays.
The telecommunication traffic prediction, be from the relevant historical record material, to obtain rule and the characteristic that the telecommunication system traffic carrying capacity changes, set up the Mathematical Modeling that to describe the telecommunication traffic variation characteristic, and then under certain required precision, utilize this mathematical model prediction telecommunication traffic of following certain specific period.The general time series analysis method that adopts of the telecommunication traffic prediction of prior art, time series analysis method is a kind of only based on the linear analysis method of telecommunication traffic data, for example, commonly used have simple linear regression analysis method, an autoregressive moving-average model (arma modeling, Auto Regression Moving Average Model) analytic approach etc., these methods all be based upon to future traffic carrying capacity change and suppose on the basis relatively slowly.But bigger for professional variable quantity, be subjected to influence festivals or holidays significant communication network simultaneously, these methods are difficult to obtain the prediction of degree of precision.
Summary of the invention
Technical problem to be solved by this invention provides a kind of telecommunication traffic Forecasting Methodology, can solve traffic carrying capacity and change greatly, is subjected to influence festivals or holidays bigger telecommunication traffic especially and predicts, thereby provide decision support for the telecommunication network management of specific period.
The present invention also provides a kind of telecommunication traffic prediction unit, to guarantee said method application in practice.
In order to address the above problem, the invention discloses a kind of telecommunication traffic Forecasting Methodology, comprising: the prediction granularity of determining telecommunication traffic; Choose the historical sample and the forecast sample of telecommunication traffic; Utilize the one-variable linear regression forecast model, calculate the first traffic carrying capacity growth rate and the first traffic carrying capacity initial value according to above-mentioned historical sample; Calculate the second traffic carrying capacity growth rate and the second traffic carrying capacity initial value according to above-mentioned forecast sample; And, calculate the first prediction traffic carrying capacity of the corresponding predicted time of historical sample institute and forecast sample corresponding predicted time second predict traffic carrying capacity; Read historical sample the actual volume of corresponding predicted time; According to the deviation of the actual volume of the corresponding predicted time of described first prediction traffic carrying capacity and the historical sample institute, the described second prediction traffic carrying capacity is adjusted, obtain after the adjustment prediction traffic carrying capacity at predicted time; Wherein, read actual volume step step any one step execution before in the end of historical sample.
Preferably, said method also comprises: generate traffic carrying capacity early warning report according to adjusted prediction traffic carrying capacity.
Preferably, said method also comprises: be presented on the computer traffic carrying capacity early warning report or printout.
Preferably, above-mentioned prediction granularity is selected to determine by the user.
Preferably, in carrying out the computer system of said method, be preset with ad hoc rules, automatically perform above-mentioned telecommunication traffic prediction flow process during condition in satisfying preset rules.
Preferably, according to described first prediction traffic carrying capacity and the historical sample the deviation of actual volume of corresponding predicted time, the described second prediction traffic carrying capacity is adjusted, adjusting the back is specially in the method for the prediction traffic carrying capacity of predicted time: the actual volume and first of the corresponding predicted time of historical sample institute is predicted the poor of traffic carrying capacity, multiply by the ratio of the second traffic carrying capacity growth rate and the first traffic carrying capacity growth rate, add the second prediction traffic carrying capacity, obtain adjusted prediction traffic carrying capacity.
Preferably, above-mentioned one-variable linear regression forecast model is specially:
The traffic growth rate k = n Σ i = 1 n ( t i y i ) - Σ i = 1 n t i Σ i = 1 n y i n Σ i = 1 n t i 2 - ( Σ i = 1 n t i ) 2
The business initial value b = 1 n ( Σ i = 1 n y i - k Σ i = 1 n t i )
Prediction traffic carrying capacity Y t=b+kt
Wherein, n represents sample size, y iThe traffic carrying capacity of representing i sample, t iThe time series of representing i sample, k are represented the traffic carrying capacity growth rate, and b represents traffic carrying capacity initial value, Y tPrediction traffic carrying capacity when the express time ordinal number is t; The described first traffic carrying capacity growth rate, the second traffic carrying capacity growth rate, the first traffic carrying capacity initial value, the second traffic carrying capacity initial value, the first prediction traffic carrying capacity and the second prediction traffic carrying capacity can adopt above-mentioned formula to calculate respectively and obtain.
Preferably, above-mentioned communication network is a mobile communications network, and network element device comprises mobile switching centre, Mobile Switching Center Server, base station manager, radio network controller, sms center or MMS center;
Preferably, above-mentioned telecommunication service is voice service, short message service, MMS or Internet service; Above-mentioned telecommunication traffic is telephone traffic, note quantity, multimedia message quantity, internet data flow or the Internet line duration.
According to another preferred embodiment of the invention, a kind of telecommunication traffic prediction unit is also disclosed, comprise: prediction granularity determining unit, sample are chosen unit, traffic carrying capacity predicting unit, traffic carrying capacity adjustment unit, and wherein: prediction granularity determining unit is used for determining the prediction granularity of telecommunication service; Sample is chosen the unit and is used for telecommunication traffic prediction granularity according to the output of prediction granularity determining unit, chooses the historical sample and the forecast sample of telecommunication traffic, and, obtain historical sample the actual volume of corresponding predicted time; The traffic carrying capacity predicting unit is used for according to the one-variable linear regression forecast model, based on historical sample and the forecast sample of originally choosing unit output, calculate the first traffic carrying capacity growth rate and the first traffic carrying capacity initial value of historical sample respectively, and the second traffic carrying capacity growth rate of forecast sample and the second traffic carrying capacity initial value, and, calculate the first prediction traffic carrying capacity of the corresponding predicted time of historical sample institute and forecast sample corresponding predicted time second predict traffic carrying capacity; The output result that the traffic carrying capacity adjustment unit is used for choosing according to sample unit and traffic carrying capacity predicting unit adjusts the prediction traffic carrying capacity.
Preferably, said apparatus also comprises traffic carrying capacity early warning report generation unit, and the prediction traffic carrying capacity at predicted time after the adjustment that is used for generating according to the traffic carrying capacity adjustment unit generates traffic carrying capacity early warning report.
Preferably, said apparatus also comprises the output unit that predicts the outcome, and is used for showing or printout adjusting the prediction traffic carrying capacity or the traffic carrying capacity early warning report of back at predicted time.
Preferably, said apparatus also comprises prediction execution unit, this prediction execution unit is preset with ad hoc rules, in satisfying ad hoc rules during preset condition, call prediction granularity determining unit automatically, sample is chosen unit, traffic carrying capacity predicting unit, traffic carrying capacity adjustment unit, traffic carrying capacity early warning report generation unit and the output unit that predicts the outcome and carried out the telecommunication traffic forecasting process.
Preferably, the concrete grammar of above-mentioned traffic carrying capacity adjustment unit adjustment prediction traffic carrying capacity is: adjust the ratio that difference that the back equals the actual volume of historical sample and the first prediction traffic carrying capacity in the prediction traffic carrying capacity of predicted time multiply by the second traffic carrying capacity growth rate and the first traffic carrying capacity growth rate, add the second prediction traffic carrying capacity that abovementioned steps obtains.
Compared with prior art, the present invention has the following advantages:
By the linear predictor of analysis of history sample traffic carrying capacity and the deviation of actual value, obtain the subscribers to telecommunication network behavior pattern, and in view of the above the linear predictor of following telecommunication traffic data is adjusted, obtain telecommunication network service amount more accurately, for the user carries out the network planning more accurately and organization and administration provide decision support.
In addition, the prediction scheme that the present invention proposes is simple and easy to usefulness, is specially adapted to the telecommunication network service amount prediction of very big festivals or holidays of traffic carrying capacity amplitude of variation, and precision of prediction is more accurate.
Description of drawings
Fig. 1 is an embodiment flow chart of telecommunication traffic Forecasting Methodology of the present invention;
Fig. 2 is an example structure block diagram of telecommunication traffic prediction unit of the present invention.
Embodiment
For above-mentioned purpose of the present invention, feature and advantage can be become apparent more, the present invention is further detailed explanation below in conjunction with the drawings and specific embodiments.
One of core idea of the present invention is, according to the linear predictor of historical traffic data and the deviation of actual value, the linear predictor of the traffic data that predict adjusted, and the accurate service amount predicts the outcome to obtain more.
With reference to Fig. 1, show the flow process of an embodiment of telecommunication traffic Forecasting Methodology of the present invention, specifically may further comprise the steps:
Step 101: the prediction granularity of determining telecommunication traffic;
Different prediction granularities can satisfy the user traffic carrying capacity on certain class network element device in different spaces and the different time is predicted.This prediction granularity is made of time interval, geographical space and three key elements of network element device type, according to determined prediction granularity, can predict the telecommunication traffic on certain class network element device of being administered in the following a certain sometime communication network zone.Can determine according to user's selection, also can in system, preset some rules and select.Described communication network can be fixed-line telephone network, data communication network, also can be mobile radio communication.Related telecommunication service can be voice service, short message service, MMS or Internet service etc.
With the mobile radio communication is example, the geographical space that the user selects can be the whole nation, certain province or somewhere are as the spatial granularity of prediction, the time interval of selecting can be one day, several days, waited time granularity in one month as prediction, take place professional based on network element device can select (the MSC of mobile switching centre, MobileSwitch Center), Mobile Switching Center Server (MSS, MSC Server), base station manager (the BSC of second generation network, Base Station Controller), radio network controller (the RNC of third generation network, Radio Network Controller), sms center (SMSC, Short Messaging Service Center) or MMS center (MMSC, Multimedia Messaging Service Center) etc.Certainly, can directly will determine that good prediction granularity is stored in the system, system carries out follow-up operation according to the prediction granularity of acquiescence.
The prediction granularity can define voluntarily according to user's needs, and general time granularity is no more than one month.
Step 102: historical sample and the forecast sample of choosing described telecommunication traffic;
According to the prediction granularity that step 101 is determined, the traffic carrying capacity of choosing a period of time before predicted time (as prediction day) from the communication network database is as forecast sample; The traffic carrying capacity of choosing a period of time before corresponding predicted time in cycle reference time is as historical sample.
A time cycle before the above-mentioned place time cycle predicted time of index futures week reference time.If with 1 year as a time cycle, with one day time granularity as prediction, also can select the first two years as reference time as the reference time the previous year of just selecting predicted day place time.As, when the user wish to predict A in 2009 economize " five. one " during voice service amount situation during International Labour Day, can be with the traffic data in 1 day～April 23 April in 2009 as forecast sample, with the traffic data in 1 day～April 23 April in 2008 as historical sample.Need to prove that cycle reference time is near more from predicted day, its precision that predicts the outcome is high more.
Step 103: utilize the one-variable linear regression forecast model, calculate the traffic carrying capacity growth rate and the traffic carrying capacity initial value of historical sample and forecast sample respectively;
Adopt one-variable linear regression forecast model Y t=b+kt calculates, and wherein b is constant (a traffic carrying capacity initial value), and k is regression parameter (a traffic carrying capacity growth rate), and t represents the time series in the monobasic linear session sequence, for example: 0,1,2,3,4... ...-3 ,-2 ,-1,0,1,2,3..., Y tTraffic data when the express time ordinal number is t;
Wherein, the computing formula of one-variable linear regression forecast model constant b (business initial value) and regression parameter k (traffic growth rate) is as follows:
b = 1 n ( Σ i = 1 n y i - k Σ i = 1 n t i ) k = n Σ i = 1 n ( t i y i ) - Σ i = 1 n t i Σ i = 1 n y i n Σ i = 1 n t i 2 - ( Σ i = 1 n t i ) 2
In the above-mentioned formula, n represents sample size, y iThe traffic carrying capacity of representing i sample, t iThe time series of representing i sample, k are represented the traffic carrying capacity growth rate, and b represents traffic carrying capacity initial value, Y tPrediction traffic carrying capacity when the express time ordinal number is t.
Corresponding to historical sample, traffic carrying capacity growth rate and traffic carrying capacity initial value are used k respectively 1And b 1Expression, corresponding to forecast sample, traffic carrying capacity growth rate and traffic carrying capacity initial value are used k respectively 2And b 2Expression.
Step 104: calculate historical sample in the traffic carrying capacity of predicted time and forecast sample traffic carrying capacity at predicted time;
The traffic carrying capacity growth rate k of the historical sample that obtains according to step 103 1With traffic carrying capacity initial value b 1, utilize one-variable linear regression anticipation function Y T1=b 1+ k 1Traffic carrying capacity predicted value when t calculates the time series try to achieve historical sample and is t is Y T1
The traffic carrying capacity growth rate k of the forecast sample that obtains according to step 103 2With traffic carrying capacity initial value b 2, utilize one-variable linear regression anticipation function Y T2=b 2+ k 2Traffic carrying capacity predicted value when t calculates the time series try to achieve forecast sample and is t is Y T2
Step 105: obtain historical sample the actual volume Y of corresponding predicted time T1';
As, when the user wish to predict A in 2009 economize " five. one " during voice service amount situation during International Labour Day, can promptly obtain actual volume with historical sample at the actual traffic data in 30 days～May 3 April in 2008 comparison other as the reference time with reference to corresponding predicted time of time.
Step 106:, adjust the traffic carrying capacity predicted value of forecast sample at predicted time according to the prediction traffic carrying capacity of historical sample and the deviation of actual volume;
Adjusted predicted time traffic carrying capacity predicted value Y T2' equal the actual volume Y of the corresponding predicted time of historical sample T1' corresponding predicted time traffic carrying capacity predicted value Y with historical sample T1Poor, multiply by forecast sample traffic growth rate k 2With historical sample traffic growth rate k 1Ratio, add the corresponding predicted time traffic carrying capacity predicted value Y of forecast sample T2Computing formula is as follows:
Y t2′＝Y t2+(Y t1′-Y t1)×k 2/k 1
In another preferred embodiment of the inventive method embodiment, can also comprise: above-mentioned predicting the outcome shown, or printout the decision-making foundation of providing convenience for the user.In addition, can also predict the outcome and early warning baseline generation traffic carrying capacity early warning report according to above-mentioned, described early warning baseline is to be provided with according to the situation of change of traffic carrying capacity, telephone traffic early warning baseline during red-letter day will be higher than telephone traffic early warning baseline at ordinary times, can set when being higher than telephone traffic early warning baseline when predicting the outcome and will report to the police, showing has the unexpected variation of traffic carrying capacity to take place.Like this, just for the telecommunication network service management, particularly huge especially operation management festivals or holidays of traffic growth amount provides good technical support.
In order to reduce operation personnel's workload, reduce the accident that human factor causes, some ad hoc ruless can also be set in system, in satisfying ad hoc rules, during preset condition, automatically perform the traffic carrying capacity forecasting process.For example, carry out the traffic carrying capacity prediction important the last week festivals or holidays automatically.
In the forecasting process of said method embodiment, the linear predictor by analyzing the traffic carrying capacity of predicted time in cycle reference time and the deviation of actual value, can obtain the user behavior pattern of predicted time, and in view of the above the linear predictor of predicted time traffic data is adjusted, obtain traffic carrying capacity of predicted time more accurately, for communication network planning and management provide decision-making foundation more accurately.In addition, this method embodiment is specially adapted to the telecommunication network service amount prediction of very big festivals or holidays of traffic carrying capacity amplitude of variation.
Method example one: traffic forecast
Below, with A in 2009 economize " five. one " voice service during International Labour Day is example, specifies telecommunication traffic Forecasting Methodology of the present invention and effect thereof.Specifically comprise the steps:
Step 1: choose A and economize during April 30 to May 3 in 2009, the telephone traffic that produce in mobile switching centre every day is as the prediction granularity.
Step 2: choose on April 23rd, 1 day 1 April in 2009 A and economize the telephone traffic that produces in mobile switching centre every day as forecast sample, as shown in table 1:
Date Telephone traffic Date Telephone traffic Date Telephone traffic
April 1 465496.6495 April 9 467208.2000 April 17 486043.2632
April 2 462500.8755 April 10 473943.7504 April 18 427970.0125
April 3 471012.9628 April 11 427970.0125 April 19 413297.4748
April 4 402057.8877 April 12 413297.4748 April 20 467536.9374
April 5 385927.7499 April 13 460312.0751 April 21 456247.0752
April 6 399570.1747 April 14 463109.8213 April 22 467407.2002
April 7 467355.2371 April 15 466978.1499 April 23 433946.9542
April 8 465943.2166 April 16 453871.0002
Table 1,1 day～April 23 April in 2009, A economized the telephone traffic (unit: Ireland) of every day
Obtain on April 23rd, 1 day 1 April in 2008 A and economize the traffic data that produces in mobile switching centre every day as historical sample, as shown in table 2.
Date Telephone traffic Date Telephone traffic Date Telephone traffic
April 1 452675.2452 April 9 430203.5111 April 17 460921.5798
April 2 450358.5456 April 10 451109.8811 April 18 451093.7980
April 3 457055.9756 April 11 449540.9667 April 19 406664.9031
April 4 397783.8744 April 12 415569.6622 April 20 398874.4792
April 5 369161.5567 April 13 402873.3033 April 21 445134.8655
April 6 379882.7867 April 14 445406.0622 April 22 441288.4802
April 7 440147.9667 April 15 436469.4611 April 23 440408.8611
April 8 427023.0967 April 16 438826.2600
Table 2,1 day～April 23 April in 2008, A economized the telephone traffic (unit: Ireland) of every day
Step 3: calculate the traffic carrying capacity growth rate and the traffic carrying capacity initial value of historical sample, and forecast sample traffic carrying capacity growth rate and traffic carrying capacity initial value.
For convenience of calculation, getting the seasonal effect in time series interlude usually is initial point, gets April 12 here respectively for initial point, i.e. t during April 12 12=0; T during April 11 11=-1; T during April 13 13=1 or the like.Have this moment:
Figure G200910243569XD00081
The computing formula of k and b can be reduced to: b = 1 23 ( Σ i = 1 23 y i ) k = Σ i = 1 23 ( t i y i ) Σ i = 1 23 t i 2
According to above-mentioned formula, with 23, every day of 1 day～2008 on April April in 2008 telephone traffic as historical sample, calculate traffic carrying capacity growth rate k 1=471.7358991, traffic carrying capacity initial value b 1=429933.701;
With 23, every day of 1 day～2009 on April April in 2009 telephone traffic as the traffic carrying capacity growth rate k of forecast sample 2=504.0300679, traffic carrying capacity initial value b 2=447782.7894.
Step 4: according to one-variable linear regression anticipation function Y T1=b 1+ k 1T calculates telephone traffic 3, every day of 30 days～2008 on May April in 2008, as the prediction traffic carrying capacity Y of historical sample T1As shown in table 3:
Date The prediction traffic carrying capacity Y of historical sample t1(unit: Ireland)
On April 30th, 2008 438424.9472
On May 1st, 2008 438896.6831
On May 2nd, 2008 439368.419
On May 3rd, 2008 439840.1549
Table 3, the prediction telephone traffic that on May 3rd, 30 days 1 April in 2008, A economized
In like manner, according to one-variable linear regression anticipation function Y T2=b 2+ k 2T calculates telephone traffic 3, every day of 30 days～2009 on May April in 2009, as the prediction traffic carrying capacity Y of forecast sample T2, as shown in table 4:
Date The prediction traffic carrying capacity Y of forecast sample t2(unit: Ireland)
On April 30th, 2009 456855.3306
On May 1st, 2009 457359.3607
On May 2nd, 2009 457863.3908
On May 3rd, 2009 458367.4208
Table 4, the prediction telephone traffic that 30 days～May 3 April in 2009, A economized
Step 5: obtain telephone traffic Y in 30 days～2008 April in 2008 actual every day on May 3, according to historical sample T1', as shown in table 5.
Date The actual telephone traffic Y of historical sample t1' (unit: Ireland)
On April 30th, 2008 492298.9383
On May 1st, 2008 435705.0411
On May 2nd, 2008 384150.1022
On May 3rd, 2008 378436.0682
Table 5, the actual telephone traffic that 30 days～May 3 April in 2008, A economized
Step 6: historical sample actual telephone traffic Y on May 3,30 days～2008 April in 2008 T1' with on May 3,30 days～2008 April in 2008 prediction telephone traffic Y T1Poor, multiply by forecast sample traffic carrying capacity growth rate k 2With historical sample traffic carrying capacity growth rate k 1Ratio, add on May 3,30 days～2009 April in 2009 prediction telephone traffic Y T2, the final adjustment back prediction telephone traffic Y that obtains T2'.Computing formula is as follows:
Y t2′＝Y t2+(Y t1′-Y t1)×k 2/k 1
Result of calculation is as shown in table 6:
Date Adjusted prediction telephone traffic Y t2' (unit: Ireland)
On April 30th, 2009 514417.4357
On May 1st, 2009 453949.2248
On May 2nd, 2009 398864.9299
On May 3rd, 2009 392759.7238
It is as shown in table 7 that table 6,30 days～May 3 April in 2009, A economized adjusted prediction traffic forecast resultant error analysis:
Date Actual telephone traffic in 2009 One-variable linear regression prediction telephone traffic Y t2 The one-variable linear regression predicated error The present invention predicts telephone traffic Y 2 Predicated error % of the present invention
April 30 503887.9877 456855.3306 9.33％ 514417.4357 2.09％
May 1 433409.8879 457359.3607 5..53％ 453949.2248 4.74％
May 2 387213.7627 457863.3908 18.25％ 398864.9299 3.01％
May 3 383643.538 458367.4208 19.48％ 392759.7238 2.38％
The present invention that table 7,30 days～May 3 April in 2009, A economized predict telephone traffic (unit: Ireland),
One-variable linear regression prediction telephone traffic and actual telephone traffic comparative result
As can be seen from Table 7, adopt forecast method error rate of the present invention to be controlled in 5%, minimal error 2.09%, worst error 4.74%; And adopt the direct predicated error change of one-variable linear regression Forecasting Methodology bigger, minimum 5.52%, maximum 19.48%; The Forecasting Methodology that the present invention's employing is described is more accurate, more effective.
Method example two: note amount prediction
For further specifying the effect of the inventive method embodiment, economizing the note quantitative forecast in 30 days～May 3 April in 2009 with A below is that example describes.Calculation procedure is identical with previous examples, lists related data at this and describes.
One, about historical sample and forecast sample
On April 23,1 day～2008 April in 2008 and note incremental data tabulation on April 23rd, 1 day 1 April in 2009 are as shown in table 8, unit: 1,000,000:
Date 2008 2009 Date 2008 2009
April 1 204.43 239.86 April 13 166.94 231.21
April 2 174.54 228.72 April 14 174.65 230.88
April 3 186.04 232.84 April 15 177.21 225.07
April 4 181.18 228.32 April 16 173.12 224.66
April 5 168.99 220.19 April 17 173.75 226.57
April 6 168.03 218.75 April 18 172.69 222.83
April 7 173.01 220.25 April 19 171.43 219.79
April 8 180.4 238.49 April 20 168.23 223.9
April 9 173.69 229.91 April 21 176.42 222.2
April 10 172.42 232.6 April 22 171.48 220.83
April 11 179.05 232.19 April 23 173.18 225.13
April 12 168.76 225.28
Table 8, the note quantity that A economized during 1～April 23 April in 2008 and 2009
Two, the business information that takes place about reality
On May 3,30 days～2008 April in 2008 and on May 3,30 days～2009 April in 2009, the actual note quantity tabulation that takes place was as shown in table 9, unit: 1,000,000:
Date 2008 2009
April 30 195.57 249.66
May 1 201.98 239.97
May 2 178.5 224.27
May 3 170.85 215.64
Table 9, the actual note quantity that A economized during 30～May 3 April in 2008 and 2009
Three, about traffic carrying capacity growth rate k and traffic carrying capacity initial value b
As shown in table 10 according to k and b that table 8 historical sample and forecast sample calculate:
Parameter 2008 2009
k -0.535573123 -0.388369565
b 175.2017391 226.9769565
The traffic growth rate of table 10, historical sample and forecast sample and traffic carrying capacity initial value parameter
Four, the prediction traffic carrying capacity that calculates according to historical sample and forecast sample
According to the one-variable linear regression forecast model, the note incremental data tabulation on 30～May 3 April that prediction obtains is as shown in table 11, unit: 1,000,000:
Date 2008 2009
April 30 173.52 219.46
May 1 172.98 219.08
May 2 172.45 218.69
May 3 171.92 218.30
Table 11, the prediction note quantity that A economized during 30～May 3 April in 2008 and 2009
Five, adjusted predicting the outcome
The Forecasting Methodology according to the present invention, predicting the outcome of drawing at last is as shown in table 12, unit: 1,000,000:
Date Adjusted prediction traffic carrying capacity in 2009
April 30 243.02
May 1 250.05
May 2 225.15
May 3 217.16
Prediction note quantity after table 12, A economizes during 30 days～May 3 April in 2009 the adjustment
Six, the error analysis that predicts the outcome is as shown in table 13:
Date Actual note quantity in 2009 One-variable linear regression prediction note quantity Monobasic is linear to return the present invention and predicts that the present invention predicts and return predicated error note quantitative error
April 30 249.66 219.46 12.09％ 243.02 2.66％
May 1 239.97 219.08 8.71％ 250.05 4.20％
May 2 224.27 218.69 2.49％ 225.15 0.39％
May 3 215.64 218.30 1.23％ 217.16 0.70％
The present invention that table 13,30 days～May 3 April in 2009, A economized predict the note number (unit: the bar number),
One-variable linear regression prediction note number and actual note are counted comparative result
As can be seen from Table 13, it is more more accurate than adopting one-variable linear regression method prediction result to adopt Forecasting Methodology prediction result of the present invention.
For aforesaid each method embodiment, simple in order to describe, so it all is expressed as a series of combination of actions, but those skilled in the art should know, the present invention is not subjected to the restriction of described sequence of movement, because according to the present invention, some step can adopt other orders or carry out simultaneously.Secondly, those skilled in the art should know that also said method embodiment all belongs to preferred embodiment, and related action and module might not be that the present invention is necessary.
With reference to Fig. 2, show the structured flowchart of telecommunication traffic prediction unit one embodiment of the present invention, specifically comprise with lower unit:
Prediction granularity determining unit 21: the prediction granularity that is used for determining telecommunication service.
Apparatus of the present invention embodiment communication network applicatory can be a fixed-line phone network, also can be mobile radio communication.When determining the prediction granularity, according to the business field (as voice service, short message service, MMS or Internet service etc.) that the user paid close attention to, determine prediction granularity based on time interval, geographical space and three key elements formations of network element device type, the time granularity of prediction can be one day, several days, one month etc., the spatial granularity of prediction can be the communication network zone, as Beijing area, Liaoning Province, Daliang City etc.; Take place professional based on network element device can be MSC, MSS, BSC, RNC, SMSC or MMSC or the like.About the confirmation method of prediction granularity, can also can in prediction granularity determining unit 21, preset the prediction granularity by predicting that granularity determining unit 21 prompting users select or input.
Sample is chosen unit 22: be used for the result according to 21 outputs of prediction granularity determining unit, choose the historical sample and the forecast sample of telecommunication service; And, obtain the actual volume of the pairing predicted time of historical sample.
According to the prediction granularity that prediction granularity determining unit 21 is determined, the traffic carrying capacity of choosing a period of time before predicted time (as prediction day) from the communication network database is as forecast sample; The traffic carrying capacity of choosing a period of time before corresponding predicted time in cycle reference time is as historical sample.
As, when the user wish to predict A in 2009 economize " five. one " during voice service amount situation during International Labour Day, can be with the traffic data in 1 day～April 23 April in 2008 as historical sample, with the traffic data in 30 days～May 3 April in 2008 comparison other as the reference time, with the traffic data in 1 day～April 23 April in 2009 as forecast sample.
Traffic carrying capacity predicting unit 23: be used for according to one-variable linear regression forecast model Y t=b+kt chooses historical sample and the forecast sample that unit 22 is exported based on sample, calculates the traffic carrying capacity growth rate and the traffic carrying capacity initial value of historical sample respectively, and the traffic carrying capacity growth rate of forecast sample and traffic carrying capacity initial value; And, be used for further calculating historical sample in the prediction traffic carrying capacity of corresponding predicted time and forecast sample prediction traffic carrying capacity at corresponding predicted time;
Wherein b is constant (a traffic carrying capacity initial value), and k is regression parameter (a traffic carrying capacity growth rate), and t represents the time series in the monobasic linear session sequence, for example: 0,1,2,3,4... ...-3 ,-2 ,-1,0,1,2,3..., Y tTraffic data when the express time ordinal number is t.
Wherein, the computing formula of one-variable linear regression forecast model constant b (business initial value) and regression parameter k (traffic growth rate) is as follows:
b = 1 n ( Σ i = 1 n y i - k Σ i = 1 n t i ) k = n Σ i = 1 n ( t i y i ) - Σ i = 1 n t i Σ i = 1 n y i n Σ i = 1 n t i 2 - ( Σ i = 1 n t i ) 2
In the above-mentioned formula, n represents sample size, y iThe traffic carrying capacity of representing i sample, t iThe time series of representing i sample, k are represented the traffic carrying capacity growth rate, and b represents traffic carrying capacity initial value, Y tPrediction traffic carrying capacity when the express time ordinal number is t.
Traffic carrying capacity adjustment unit 24: the output result who is used for choosing according to sample unit 22 and traffic carrying capacity predicting unit 23 adjusts the prediction traffic carrying capacity, obtains the traffic carrying capacity at predicted time.
Wherein, adjust the prediction traffic carrying capacity Y of back at predicted time T2' equal the actual volume Y of the corresponding predicted time of historical sample T1' with the prediction traffic carrying capacity Y of the corresponding predicted time of historical sample T1Poor, multiply by the traffic carrying capacity growth rate k of forecast sample 2Traffic carrying capacity growth rate k with historical sample 1Ratio, add the prediction traffic carrying capacity of the forecast sample of traffic carrying capacity predicting unit 23 output at predicted time.
Computing formula is as follows:
Y t2′＝Y t2+(Y t1′-Y t1)×k 2/k 1
In another preferred embodiment of apparatus of the present invention embodiment, also comprise the early warning report generation unit 25 and the output unit 26 that predicts the outcome, wherein, early warning report generation unit 25 is used for the adjusted prediction traffic carrying capacity according to 24 generations of traffic carrying capacity adjustment unit, generates traffic carrying capacity early warning report; The output unit 26 that predicts the outcome is used for the described back of adjusting is shown or printout in the prediction traffic carrying capacity or the traffic carrying capacity early warning report of predicted time.
In addition, apparatus of the present invention embodiment can also comprise prediction execution unit 27, be preset with ad hoc rules in this prediction execution unit 27, during preset condition, prediction execution unit 27 automatic sequence calls prediction granularity determining units 21, sample are chosen parts such as unit 22, traffic carrying capacity predicting unit 23, traffic carrying capacity adjustment unit 24, early warning report generation unit 25 and the output unit 26 that predicts the outcome and are carried out the telecommunication traffic forecasting process in satisfying ad hoc rules.
Need to prove that said apparatus embodiment belongs to preferred embodiment, related unit might not be that the present invention is necessary.
Each embodiment in this specification all adopts the mode of going forward one by one to describe, and what each embodiment stressed all is and the difference of other embodiment that identical similar part is mutually referring to getting final product between each embodiment.For device embodiment of the present invention, because it is similar substantially to method embodiment, so description is fairly simple, relevant part gets final product referring to the part explanation of method embodiment.
More than a kind of telecommunication traffic Forecasting Methodology provided by the present invention and device are described in detail, used specific case herein principle of the present invention and execution mode are set forth, the explanation of above embodiment just is used for helping to understand method of the present invention and core concept thereof; Simultaneously, for one of ordinary skill in the art, according to thought of the present invention, the part that all can change in specific embodiments and applications, in sum, this description should not be construed as limitation of the present invention.

Claims (12)

1. a telecommunication traffic Forecasting Methodology is characterized in that, described method is carried out by computer, and described method comprises:
Determine the prediction granularity of described telecommunication traffic;
Choose the historical sample and the forecast sample of described telecommunication traffic;
Utilize the one-variable linear regression forecast model, calculate the first traffic carrying capacity growth rate and the first traffic carrying capacity initial value according to described historical sample; Calculate the second traffic carrying capacity growth rate and the second traffic carrying capacity initial value according to described forecast sample; And, calculate described historical sample corresponding predicted time the corresponding predicted time of first prediction traffic carrying capacity and the forecast sample institute second predict traffic carrying capacity;
Read historical sample the actual volume of corresponding predicted time;
According to the deviation of the actual volume of the corresponding predicted time of described first prediction traffic carrying capacity and the historical sample institute, the described second prediction traffic carrying capacity is adjusted, obtain after the adjustment prediction traffic carrying capacity at predicted time;
Wherein, described read historical sample the actual volume step of corresponding predicted time can be in the end any one step before step carry out.
2. the method for claim 1 is characterized in that, described method also comprises: generate traffic carrying capacity early warning report according to described adjusted prediction traffic carrying capacity.
3. method as claimed in claim 2 is characterized in that, described method also comprises: described traffic carrying capacity early warning report is presented on the described computer, or printout.
4. as the described method of one of claim 1～3, it is characterized in that:
Described prediction granularity is selected to determine by the user; Or
In described computer system, be preset with ad hoc rules, automatically perform above-mentioned telecommunication traffic prediction flow process during condition in satisfying preset rules.
5. the method for claim 1, it is characterized in that, the deviation of described actual volume according to the corresponding predicted time of described first prediction traffic carrying capacity and the historical sample institute is adjusted the described second prediction traffic carrying capacity, obtains after the adjustment to be specially in the method for the prediction traffic carrying capacity of predicted time:
Historical sample the actual volume of corresponding predicted time and the first prediction traffic carrying capacity poor, multiply by the ratio of the second traffic carrying capacity growth rate and the first traffic carrying capacity growth rate, add the second prediction traffic carrying capacity, obtain adjusted prediction traffic carrying capacity at predicted time.
6. the method for claim 1 is characterized in that, described one-variable linear regression forecast model is specially:
The traffic growth rate k = n Σ i = 1 k ( t i y i ) - Σ i = 1 k t i Σ i = 1 k y i n Σ i = 1 k t i 2 - ( Σ i = 1 k t i ) 2
The business initial value b = 1 n ( Σ i = 1 n y i - k Σ i = 1 n t i )
Prediction traffic carrying capacity Y t=b+kt
Wherein, n represents sample size, y iThe traffic carrying capacity of representing i sample, t iThe time series of representing i sample, k are represented the traffic carrying capacity growth rate, and b represents traffic carrying capacity initial value, Y tPrediction traffic carrying capacity when the express time ordinal number is t;
The described first traffic carrying capacity growth rate, the second traffic carrying capacity growth rate, the first traffic carrying capacity initial value, the second traffic carrying capacity initial value, the first prediction traffic carrying capacity and the second prediction traffic carrying capacity can adopt above-mentioned formula to calculate respectively and obtain.
7. the method for claim 1 is characterized in that:
Described communication network is a mobile communications network, and described network element device comprises mobile switching centre, Mobile Switching Center Server, base station manager, radio network controller, sms center or MMS center;
And/or
Described telecommunication service is voice service, short message service, MMS or Internet service; Described telecommunication traffic is telephone traffic, note quantity, multimedia message quantity, internet data flow or the Internet line duration.
8. a telecommunication traffic prediction unit is characterized in that, described device comprises that prediction granularity determining unit, sample choose unit, traffic carrying capacity predicting unit, traffic carrying capacity adjustment unit, wherein:
Described prediction granularity determining unit is used for determining the prediction granularity of described telecommunication traffic;
Described sample is chosen the unit and is used for predicting granularity according to the telecommunication traffic of described prediction granularity determining unit output, chooses the historical sample and the forecast sample of described telecommunication traffic; And, obtain historical sample the actual volume of corresponding predicted time;
Described traffic carrying capacity predicting unit is used for according to the one-variable linear regression forecast model, choose the historical sample and the forecast sample of unit output based on described sample, calculate the first traffic carrying capacity growth rate and the first traffic carrying capacity initial value of described historical sample respectively, and the second traffic carrying capacity growth rate of described forecast sample and the second traffic carrying capacity initial value; And, calculate described historical sample corresponding predicted time the corresponding predicted time of first prediction traffic carrying capacity and the forecast sample institute second predict traffic carrying capacity;
Described traffic carrying capacity adjustment unit is used for adjusting described prediction traffic carrying capacity according to the result of calculation that described sample is chosen unit and traffic carrying capacity predicting unit, obtains the prediction traffic carrying capacity at predicted time.
9. device as claimed in claim 8 is characterized in that, described device also comprises early warning report generation unit, and the prediction traffic carrying capacity at predicted time after the adjustment that is used for generating according to described traffic carrying capacity adjustment unit generates traffic carrying capacity early warning report.
10. device as claimed in claim 8 is characterized in that described device also comprises the output unit that predicts the outcome, and is used for described adjusted prediction traffic carrying capacity or traffic carrying capacity early warning report are shown or printout.
11. device as claimed in claim 10, it is characterized in that, described device also comprises prediction execution unit, described prediction execution unit is preset with ad hoc rules, calls described prediction granularity determining unit during preset condition automatically in satisfying described ad hoc rules, sample is chosen unit, traffic carrying capacity predicting unit, traffic carrying capacity adjustment unit, early warning report generation unit and the output unit that predicts the outcome and carried out the telecommunication traffic forecasting process.
12. device as claimed in claim 8, it is characterized in that, the method of described traffic carrying capacity adjustment unit adjustment prediction traffic carrying capacity is specially: describedly adjust the ratio that difference that the back equals the actual volume of the corresponding predicted time of historical sample institute and the first prediction traffic carrying capacity in the prediction traffic carrying capacity of predicted time multiply by the second traffic carrying capacity growth rate and the first traffic carrying capacity growth rate, add that second predicts traffic carrying capacity.
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