CN115204535B - Purchasing business volume prediction method based on dynamic multivariate time sequence and electronic equipment - Google Patents
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Abstract
The invention discloses a purchasing business volume prediction method based on a dynamic multivariate time sequence and electronic equipment. The invention solves the problem of incomprehensive manual feature extraction in purchasing traffic prediction, and the time convolution module provided by the invention extracts the nonlinear feature of a static time sequence. The invention solves the problems of gradient disappearance and gradient explosion caused by overlong time interval when long-period time sequence prediction is carried out by purchasing traffic prediction, and the proposed multi-stage attention module adaptively selects important characteristics to model the dependency relationship between states of long time interval. The invention solves the problem that a nonlinear module cannot process the prediction of the dynamic time sequence in the purchasing traffic prediction, and the proposed autoregressive module processes the dynamic time sequence with unfixed period.
Description
Technical Field
The invention belongs to the field of artificial intelligence in computer science, relates to a purchasing traffic prediction method and electronic equipment, and particularly relates to a purchasing traffic prediction method and electronic equipment based on a dynamic multivariate time sequence in robot-oriented process automation.
Background
In recent years, purchasing management and supply chain management play an extremely important role in enterprise development and competition processes, a large amount of purchasing business volume prediction work exists in a traditional purchasing process, the purchasing business volume prediction means that the purchasing business volume of a next period is predicted based on large batch of purchasing data in the purchasing process, and efficient and reliable purchasing business volume prediction can help an enterprise to make a better purchasing decision in the next period, so that cost is saved, and purchasing efficiency is improved. However, the purchasing data required for purchasing traffic prediction is often of various types, most of the purchasing data are time series data and have long duration, and part of the data have no fixed period and belong to dynamic multivariate time series data. Time series refers to a series of data collected at regular intervals, and multivariate time series refers to a multivariate time series, i.e., a plurality of variables recorded over time. The dynamic multivariate time series data refers to multivariate time series data with an unfixed period and dynamically changing sequence length.
The traditional purchasing traffic prediction method is to manually select features and train a fully-connected neural network for prediction. However, the problem of incomplete feature extraction by manpower, the long-term dependence relationship of long-period time series cannot be modeled by a fully-connected neural network, and dynamic time series data cannot be processed, so that the search for purchasing traffic prediction of dynamic multivariate time series in robot process automation is one of the targets pursued by many enterprises.
The Robot Process Automation (RPA) is a new type artificial intelligent virtual process automation robot, which can simulate the operation of human on the computer interface by specific rules and automatically execute the corresponding process task according to the rules to replace or assist the human to complete the related computer operation. The demand for Robot Process Automation (RPA) has increased rapidly in recent years by various industries, and it is estimated that as many as 90% of medium and large organizations select a Robot Process Automation (RPA) solution by 2020. Robot Process Automation (RPA) has been widely used in various fields, and in recent years, with the advent and development of corporate financial sharing centers, robot Process Automation (RPA) has also been increasingly used in the fields of procurement management and supply chain management. The Robot Process Automation (RPA) has the advantages of high working efficiency, high accuracy, high expandability, compliance and safety, capability of automating simple tasks, capability of reducing procurement management cost and capability of freeing up space for more strategic activities.
Disclosure of Invention
The invention provides a purchasing traffic prediction method and electronic equipment based on dynamic multivariate time sequence in robot process automation, which are oriented to the problems that the characteristics are not comprehensively extracted manually in the purchasing traffic prediction, when the purchasing traffic prediction is carried out for long-period time sequence prediction, the problems of gradient disappearance and gradient explosion caused by overlong time interval and the demand for the purchasing traffic prediction of the dynamic time sequence.
The method adopts the technical scheme that: a purchasing traffic prediction method based on dynamic multivariate time sequence inputs data obtained by a robot flow automatic data acquisition network into a robot flow automatic purchasing traffic prediction network to predict purchasing traffic;
the robot process automation data acquisition network extracts index data required by service purchase quantity according to the keyword table, arranges the data according to time, and obtains time sequence data of N indexes as an external sequenceWherein N represents the number of indexes, and T is the length of the external sequence; each index is a variable, the firstnThe time series data of each index is the secondnSingle univariate time series dataIs as followsnTime series data of the individual indices; the time sequence data of all indexes form dynamic multi-element time sequence data which are used as input variables X of the robot process automatic purchase business volume prediction network;
the robot process automatic purchasing business volume prediction network comprises a time convolution module, a multi-stage attention module, a full connection layer and an autoregressive module;
the time convolution module is characterized in that the time convolution module is composed of K convolution kernels, the size of each convolution kernel is F multiplied by M, F is the width of each convolution kernel, M is the depth of each convolution kernel, time sequence data of other N static indexes except the purchasing business volume index are used as an input external sequence X to be input into each convolution kernel, and finally an output characteristic sequence H with the size of P multiplied by K is obtained, wherein P is PIs also the length of the univariate time series;
the multi-stage attention module, comprising a sequenceA first stage attention layer, a second stage attention layer and a temporal attention layer connected; the output variable H passes through the first stage attention layer, an external sequence is selected in a self-adaptive mode for learning, the output external sequence of the first stage attention layer is connected with a target sequence corresponding to time through the second stage attention layer, finally the hidden states in the first two layers are combined through the time attention layer to learn the characteristics of longer time dependence, and the characteristics of longer time dependence are output;
The autoregressive module takes the dynamic multivariate time sequence data as an input vector X and outputs the input vector X;
Wherein the content of the first and second substances,in order to be a super-parameter,is a learnable weight vector, X is an input vector,Uin order to input the weight matrix, the weight matrix is input,bin order to be a vector of the offset,is the noise at the time t, and,is variance, independent of time;
the robot process automation data acquisition network finally outputs the predicted value of the minimized purchasing business volume。
The technical scheme adopted by the electronic equipment is as follows: an electronic device for forecasting procurement traffic based on a dynamic multivariate time series, comprising:
one or more processors;
a storage device for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the dynamic multivariate time series-based procurement traffic prediction method.
Compared with the prior art, the invention has the advantages and positive effects mainly embodied in the following aspects:
(1) The invention provides a purchasing business volume prediction method for a dynamic multi-element time sequence in robot process automation. The invention solves the problem of incomplete feature extraction in the traditional purchasing traffic prediction process, and the time convolution module provided by the invention extracts the nonlinear feature of the static time sequence.
(2) The invention solves the problems of gradient disappearance and gradient explosion caused by overlong time interval when long-period time sequence prediction is carried out by purchasing traffic prediction, and the proposed multi-stage attention module adaptively selects important characteristics to model the dependency relationship between states of long time interval.
(3) The invention solves the problem that a nonlinear module cannot process the prediction of the dynamic time sequence required in the purchasing traffic prediction, and the proposed autoregressive module processes the dynamic time sequence with unfixed period.
Drawings
FIG. 1 is a general framework diagram of a method according to an embodiment of the invention;
FIG. 2 is a schematic loss diagram of a Purchases data set model according to an embodiment of the present invention;
FIG. 3 is a NASDAQ100 data set model loss diagram of an embodiment of the present invention;
FIG. 4 is a schematic diagram of model prediction results on Purchases data sets according to an embodiment of the present invention;
FIG. 5 is a graph illustrating the prediction of the model on the NASDAQ100 dataset according to an embodiment of the present invention;
fig. 6 is a schematic diagram of the prediction effect of different time steps according to an embodiment of the present invention.
Detailed Description
For the purpose of facilitating understanding and implementing the invention by those of ordinary skill in the art, the invention is described in further detail below with reference to the accompanying drawings and examples, it being understood that the examples described herein are for purposes of illustration and explanation only and are not intended to be limiting.
Referring to fig. 1, the method for forecasting the purchasing traffic based on the dynamic multivariate time sequence provided by the present invention inputs the data obtained through the robot process automation data acquisition network into the robot process automation purchasing traffic forecasting network to forecast the purchasing traffic;
according to the robot process automation data acquisition network, index data required by service purchase quantity are extracted according to the keyword table, the data are sorted according to time, and time sequence data of N indexes are obtained and serve as an external sequenceWherein N represents the number of indexes, and T is the length of the external sequence; each index is a variable, the firstnThe time series data of each index is the secondnSingle univariate time series dataIs as followsnTime series data of the individual indices; and the time sequence data of all indexes form dynamic multi-element time sequence data which are used as input variables X of the robot process automatic purchase traffic prediction network.
The keyword table adopted in the embodiment is shown in table 1 below;
TABLE 1
The robot flow automatic procurement business volume prediction network comprises a time convolution module, a multi-stage attention module, a full connection layer and an autoregression module;
the time convolution module of this embodiment has K convolution kernels, and the size of each convolution kernel is F × M, where F is the width of the convolution kernel and M is the depth of the convolution kernel.
In this embodiment, a convolution neural module is first constructed, convolution kernels of the convolution neural module are set, and the size of each convolution kernel is F × M by K convolution kernels, where F is the width of the convolution kernel and M is the depth of the convolution kernel. Inputting the input variable X into K convolution kernels in sequence, inputting X into the second convolution kernelkOutput variable obtained by convolution kernelWherein W is k Weight parameter of k convolution kernel represents convolution operation, RELU is nonlinear activation function, and RELU function is defined as;b k Is a bias vector.
In this embodiment, after the time series data of N static indicators other than the index of the purchase traffic is input as the input external sequence X into the convolution kernel, the output feature sequence H with the size of P × K is finally obtained, where P ish k Is also the length of the univariate time series.
The multi-stage attention module of the embodiment comprises a first stage attention layer, a second stage attention layer and a time attention layer which are connected in sequence; the output variable H passes through the first stage attention layer, an important external sequence is selected in a self-adaptive mode for learning, the output external sequence of the first stage attention layer is connected with a target sequence corresponding to time through the second stage attention layer, finally the hidden states in the first two layers are combined through the time attention layer to learn the characteristics of longer time dependence, and the characteristics of longer time dependence are output;
In the first stage attention layer of this embodiment, the output signature sequence H of the previous step is used as the input sequence X of this step, and the memory cell state S at the time of the long short term memory network (LSTM) t-1 is determined according to the target sequence y without considering the target sequence y t-1 And hidden layer state h at time t-1 t-1 Constructing the kth input sequence X k Attention value at time t:
Wherein the content of the first and second substances,is a parameter that needs to be learned,in the form of a matrix of state weights,uis a hidden layer state h t-1 I.e., the number of hidden layer nodes of the long-short term memory network (LSTM),U m in order to input the weight matrix, the weight matrix is input,b m for the bias vector, tanh () is a nonlinear activation function,X k is as followskThe number of the input sequences is one,;
according totInput attention of time of dayCalculating attention weightsCalculating first stage attention output(ii) a Andthidden state at time of dayIn whichIs a long short term memory network LSTM (LSTM) used as an encoder.
The second stage attention layer of this embodiment constructs an input sequence of the second attention stage at time t, the secondkAn input sequenceWherein isyThe sequence of interest is a sequence of interest,representing a vertical concatenation of matrices;
WhereinIs a parameter to be learned;in the form of a matrix of state weights,uis a hidden layer state h t-1 The number of hidden layer nodes of the long short term memory network (LSTM),U s in order to input the weight matrix, the weight matrix is input,b s for the bias vector, tanh () is a nonlinear activation function;
WhereinIn order to be a hyper-parameter,for the purpose of the second stage attention weighting,is the second stage input sequence.
The first two phases focus more on the relationship between sequences in the time series, and for better prediction of the target value, it is necessary to learn the correlation to the time series data for a longer time interval. This phase is a temporal attention phase, with temporal attention layers, learning longer-time dependencies based on the hidden states of the first two phases and the hidden state with the predicted target.
Wherein the content of the first and second substances,in the form of a matrix of state weights,in order to input the weight matrix, the weight matrix is input,for the bias vector, the hidden layer state of the long short term memory network (LSTM) as decoder at time t-1,The long-short term memory network (LSTM) as an encoder in the first two stages is formed by transversely splicing the states of memory units at the time t-1,the memory unit state of the long-short term memory network (LSTM) at the current stage at the moment k,output for the second attention stage;
According to context vectorC t Calculating,Calculating the time attention stagetHidden layer state of long-short term memory network (LSTM) as decoder at time of dayIn whichFor long short term memory networks (LSTM) to be used as decoders,in the form of a matrix of state weights,is a bias vector;
Wherein the content of the first and second substances,as a state weight matrix,In order to be a vector of the offset,is the hidden state of the decoder at time t,C t is a context vector.
The autoregressive module of this embodiment outputs the dynamic multivariate time series data as an input vector X;
Wherein the content of the first and second substances,in order to be a hyper-parameter,is a learnable weight vector, X is an input vector,Uin order to input the weight matrix, the weight matrix is input,bin order to be a vector of the offset,is the noise at the time of the t-time,is variance, independent of time;
the robot process automation data acquisition network of the embodiment finally outputs the predicted value of the minimized purchasing traffic. And finally, the robot process automation stores the prediction result to a corresponding position.
The robot process automation data acquisition network of the embodiment is a trained robot process automation data acquisition network; training processUsing a small batch stochastic gradient descent method and an ADAM optimizer, the objective function is to minimize the predicted value of procurement trafficy t And true valueyMean square error between。
The invention is further illustrated by the following experiments. To test the performance of the present invention, two data sets were used, as shown in table 2.
TABLE 2
Data set | Test set | Test set | Description of data set |
Purchases | 40000 | 10000 | |
NASDAQ | |||
100 | 32000 | 8560 | Stock price data |
Shopping data sets (Purchases): shopping data sets (Purchases) are Kaggle-based "get valuable shopper" challenge data sets that contain the shopping history of thousands of individuals, with each user record containing over a year transactions, including many fields such as product name, chain of stores, quantity, and date of purchase. In the experiment, the data was processed over time to obtain a time series shopping data set (containing 50000 samples), of which 4000 were used as training set and the rest were used as test set.
In order to measure the effectiveness of the purchasing traffic prediction method based on the dynamic multivariate time sequence, two evaluation indexes are used in the experiment: root Mean Square Error (RMSE) and mean square error (MAE). Suppose thatIs the firstiAt a time of a sampletThe true value of the time of day,is the firstiAt a time of a sampletThe predicted value of the time model, RMSE, is defined as:MAE is defined as:the smaller the two values are, the better the prediction effect is, wherein N is the number of samples. Parameters, such as time step T of a time sequence, need to be set in the purchasing business volume prediction method based on the dynamic multi-element time sequence. In order to reduce the experimental error, 10 experiments were repeated, and the average of 10 experiments was taken as the final result.
As shown in Table 3, the dynamic multivariate time series-based prediction method for the procurement traffic volume is about 0.3 in both RMSE and MAE on two data sets, and the effect is better than that of the traditional method (LSTM). The method has different predictive effects on different data sets because the data characteristics of different data sets are different.
TABLE 3 evaluation results
Please refer to fig. 2 and fig. 3, which are schematic diagrams of Purchases data set model loss and NASDAQ100 data set model loss, respectively; as can be seen from fig. 2 and 3, the dynamic multivariate time sequence-based purchasing traffic prediction method has a good training convergence effect on two different data sets, and proves that the method has good prediction performance on both periodic and non-periodic time sequences. As can be seen from fig. 2 and 3, the training loss of the model decreases with the increase of the number of training rounds, which proves the effectiveness of the model in predicting the time series.
In order to observe the model prediction effect, the prediction results of the model on two different data sets are plotted in fig. 4 and 5. As can be seen from fig. 4 and 5, when the training data is sufficient, the model prediction result is very close to the real result. In addition, as the training time is increased, the superposition effect of the predicted value and the actual value of the model on the two data sets is better and better. The solid line represents the predicted value and the dotted line represents the true value.
In order to further optimize the parameters of the model, the model performance at different time steps on different data sets was tested. The experimental result is shown in fig. 6, and it can be seen from fig. 6 that, on the Purchases data set, when the time step is 4, the performance of the model is the best; on the NASDAQ100 s dataset, the performance of the model was best when the time step was 9.
The invention provides a purchasing business volume prediction method based on a dynamic multivariate time sequence in robot-oriented process automation. The invention solves the problem of incomprehensive manual feature extraction in purchasing business volume prediction, and the time convolution module provided extracts the nonlinear features of the static time sequence. The invention solves the problems of gradient disappearance and gradient explosion caused by overlong time interval when long-period time sequence prediction is carried out by purchasing traffic prediction, and the proposed multi-stage attention module adaptively selects important characteristics to model the dependency relationship between states of long time interval. The invention solves the problem that a nonlinear module cannot process the prediction of the dynamic time sequence in the purchasing traffic prediction, and the proposed autoregressive module processes the dynamic time sequence with unfixed period.
The invention can provide more accurate and comprehensive purchasing business volume prediction method for users in more fields of artificial intelligence, purchasing management and the like.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A purchasing business volume prediction method based on dynamic multivariate time sequence is characterized in that: inputting data obtained through the robot process automation data acquisition network into a robot process automation purchasing business volume prediction network to predict purchasing business volume;
the robot process automation data acquisition network extracts index data required by service purchase quantity according to the keyword table, arranges the data according to time, and obtains time sequence data of N indexes as an external sequenceWherein N represents the number of indexes, and T is the length of the external sequence; each index is a variable, the firstnThe time series data of each index isnSingle univariate time series dataIs a firstnTime sequence of individual indexesColumn data; the time sequence data of all indexes form dynamic multi-element time sequence data which are used as input variables X of the robot process automatic purchase business volume prediction network;
the robot process automatic purchasing business volume prediction network comprises a time convolution module, a multi-stage attention module, a full connection layer and an autoregressive module;
the time convolution module is characterized in that K convolution kernels are used, the size of each convolution kernel is F multiplied by M, wherein F is the width of each convolution kernel, M is the depth of each convolution kernel, time sequence data of other N static indexes except the purchasing traffic indexes are used as an input external sequence X to be input into each convolution kernel, and finally an output characteristic sequence H with the size of P multiplied by K is obtained, wherein P is PIs also the length of the univariate time series;
the multi-stage attention module comprises a first stage attention layer, a second stage attention layer and a time attention layer which are connected in sequence; the output characteristic sequence H passes through the first stage attention layer, an external sequence is selected in a self-adaptive mode for learning, the output external sequence of the first stage attention layer is connected with a target sequence corresponding to time through the second stage attention layer, finally the hidden states in the first two layers are combined through the time attention layer to learn characteristics dependent for longer time, and the characteristics dependent for longer time are output;
The autoregressive module takes the dynamic multivariate time sequence data as an input vector X and outputs the input vector X;
Wherein, the first and the second end of the pipe are connected with each other,in order to be a hyper-parameter,is a learnable weight vector, X is an input vector,Uin order to input the weight matrix, the weight matrix is input,bin order to be a vector of the offset,is the noise at the time t, and,is variance, independent of time;
2. The method of claim 1, wherein the method for forecasting procurement traffic based on dynamic multivariate time series comprises: in the time convolution module, input variable X is input into K convolution kernels in sequence, and X is input into the second convolution kernelkOutput variable obtained by convolution kernelWhereinW k Is a firstkWeight parameter of convolution kernel represents convolution operation, RELU is nonlinear activation function, and RELU function is defined as;b k Is a bias vector.
3. The method of claim 1 for forecasting procurement traffic based on dynamic multivariate time seriesThe method is characterized in that: in the attention layer of the first stage, the output characteristic sequence H of the previous stage is taken as the input sequence X of the first stage, and the target sequence y is not considered, and the state S of the memory unit at the moment of the long-short term memory network LSTMt-1 is determined according to the state of the memory unit t-1 And hidden layer state h at time t-1 t-1 Constructing the kth input sequence X k Attention value at time t:
Wherein the content of the first and second substances,is a parameter that needs to be learned,in the form of a matrix of state weights,uis a hidden layer state h t-1 The dimension of (a), i.e. the number of hidden layer nodes of the long-short term memory network LSTM,U m in order to input the weight matrix, the weight matrix is input,b m in order to be a vector of the offset,in order to be a non-linear activation function,X k is as followskThe number of the input sequences is one,;
4. The method of claim 3, wherein the method for forecasting procurement traffic based on dynamic multivariate time series comprises: the second stage attention layer is constructed in a second attention stagetInput sequence of moments, nokAn input sequenceWherein isyThe sequence of the object is determined,representing a vertical concatenation of matrices;
WhereinIs a parameter to be learned;in the form of a matrix of state weights,uis a hidden layer state h t-1 The dimension of (a), i.e. the number of hidden layer nodes of the long-short term memory network LSTM,U s in order to input the weight matrix, the weight matrix is input,b s for the bias vector, tanh () is a nonlinear activation function;
5. The method of claim 4, wherein the method for forecasting procurement traffic based on dynamic multivariate time series comprises the following steps: the temporal attention layer, first computing intermediate states:
Wherein the content of the first and second substances,in the form of a matrix of state weights,in order to input the weight matrix, the weight matrix is input,for the bias vector, the long-short-term memory network LSTM as decoder has hidden layer state at t-1,The long-short term memory network LSTM as an encoder in the first two stages is formed by transversely splicing the states of memory units of the long-short term memory network LSTM at the time t-1,the memory unit state of the long-short term memory network LSTM at the moment k at the current stage,for the second attentionOutputting force stage;
According to context vectorC t Calculating,Calculating the time attention stagetHidden layer state of long-short term memory network LSTM as decoder at time of dayWhereinFor the long short term memory network LSTM to be used as a decoder,is the status rightThe weight of the matrix is determined by the weight,is a bias vector;
6. The method for forecasting procurement traffic based on dynamic multivariate time series as claimed in any one of claims 1 to 5, characterized in that: the robot process automatic data acquisition network is a trained robot process automatic data acquisition network; the training process adopts a small batch random gradient descent method and an ADAM optimizer, and the objective function is the predicted value of the minimized purchasing trafficy t And true valueyMean square error of。
7. An electronic device for forecasting procurement traffic based on a dynamic multivariate time series, comprising:
one or more processors;
a storage device for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the dynamic multivariate time series based procurement traffic prediction method of any of claims 1-6.
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US11681914B2 (en) * | 2020-05-08 | 2023-06-20 | International Business Machines Corporation | Determining multivariate time series data dependencies |
WO2021257610A1 (en) * | 2020-06-15 | 2021-12-23 | Spartan Capital Intelligence, Llc | Time series forecasting and visualization methods and systems |
CN111880998B (en) * | 2020-07-30 | 2022-09-02 | 平安科技(深圳)有限公司 | Service system anomaly detection method and device, computer equipment and storage medium |
CN114493014A (en) * | 2022-01-28 | 2022-05-13 | 湖南大学 | Multivariate time series prediction method, multivariate time series prediction system, computer product and storage medium |
CN114881354A (en) * | 2022-05-31 | 2022-08-09 | 支付宝(杭州)信息技术有限公司 | Method and device for predicting multivariate time series |
CN114818515A (en) * | 2022-06-24 | 2022-07-29 | 中国海洋大学 | Multidimensional time sequence prediction method based on self-attention mechanism and graph convolution network |
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