US 2024/0202579 A1Published application

Time series prediction execution based on deviation risk evaluation

AssigneeSap SE

InventorJacques Doan Huu

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Abstract

The present disclosure relates to computer-implemented methods, software, and systems for identifying data patterns based on data observations collected as time series data. A cross-validation assessment of a plurality of predictive models is performed. Based on the cross-validation assessment, a respective deviation risk is determined. The respective deviation risk is determined based on comparing forecasting variability distribution for a validation data set during the cross-validation assessment with forecasting variability distribution for test values from a test data set. The test data set represents forecasted values generated based on a respective predictive model for a future horizon. A predictive model can be excluded based on evaluating deviation risks of each of the predictive models. A model selection of a candidate model from the set of candidate predictive models is performed. The candidate model is selected based on evaluation of accuracy of the set of candidate predictive model according to the cross-validation assessment.

Claims20

Dependent claims

A computer-implemented method, comprising:

performing a cross-validation assessment of a plurality of predictive models, wherein the cross-validation assessment is based on time series data that is divided into an estimation data set and a validation data set, and wherein the estimation data set is used as training data;
based on the cross-validation assessment, determining, for each predictive model, a respective deviation risk, wherein the respective deviation risk is determined based on comparing forecasting variability distribution for the validation data set during the cross-validation assessment with forecasting variability distribution for test values from a test data set, wherein the test data set represents forecasted values generated based on a respective predictive model for a future horizon;
excluding one or more predictive models of the plurality of predictive models to define a set of candidate predictive models, wherein the excluding is based on evaluating deviation risks of each of the predictive models of the plurality of predictive models;
performing a model selection of a candidate model from the set of candidate predictive models, wherein the candidate model is selected based on evaluation of accuracy of the set of candidate predictive model according to the cross-validation assessment;
and
providing the candidate model for execution of a prediction for a requested time horizon.

3 more elements

A non-transitory, computer-readable medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

performing a cross-validation assessment of a plurality of predictive models, wherein the cross-validation assessment is based on time series data that is divided into an estimation data set and a validation data set, and wherein the estimation data set is used as training data;
based on the cross-validation assessment, determining, for each predictive model, a respective deviation risk, wherein the respective deviation risk is determined based on comparing forecasting variability distribution for the validation data set during the cross-validation assessment with forecasting variability distribution for test values from a test data set, wherein the test data set represents forecasted values generated based on a respective predictive model for a future horizon;
excluding one or more predictive models of the plurality of predictive models to define a set of candidate predictive models, wherein the excluding is based on evaluating deviation risks of each of the predictive models of the plurality of predictive models;
performing a model selection of a candidate model from the set of candidate predictive models, wherein the candidate model is selected based on evaluation of accuracy of the set of candidate predictive model according to the cross-validation assessment;
and
providing the candidate model for execution of a prediction for a requested time horizon.

3 more elements

A computer-implemented system comprising:

one or more processors;
and
one or more computer-readable memories coupled to the one or more processors and having instructions stored thereon that are executable by the one or more processors to perform operations comprising:
performing a cross-validation assessment of a plurality of predictive models, wherein the cross-validation assessment is based on time series data that is divided into an estimation data set and a validation data set, and wherein the estimation data set is used as training data;
based on the cross-validation assessment, determining, for each predictive model, a respective deviation risk, wherein the respective deviation risk is determined based on comparing forecasting variability distribution for the validation data set during the cross-validation assessment with forecasting variability distribution for test values from a test data set, wherein the test data set represents forecasted values generated based on a respective predictive model for a future horizon;
excluding one or more predictive models of the plurality of predictive models to define a set of candidate predictive models, wherein the excluding is based on evaluating deviation risks of each of the predictive models of the plurality of predictive models;
performing a model selection of a candidate model from the set of candidate predictive models, wherein the candidate model is selected based on evaluation of accuracy of the set of candidate predictive model according to the cross-validation assessment;
and
providing the candidate model for execution of a prediction for a requested time horizon.

6 more elements

Dependencies are inferred from the claim text.