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US 10,379,502 B2Granted
Control system with machine learning time-series modeling
AssigneeAccenture Global Solutions Limited
InventorsSanjay Sharma, Nilesh Kumar Gupta, Samik Adhikary, Pinaki Asish Ghosh
Life of this patent
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Abstract
An unsupervised machine learning model can make prediction on time series data. Variance of time-varying parameters for independent variables of the model may be restricted for continuous consecutive time intervals to minimize overfitting. The model may be used in a control system to control other devices or systems. If predictions for the control system are for a higher granularity time interval than the current mode, the time-varying parameters of the model are modified for the higher granularity time interval.
Claims18
A control system to control devices based on an unsupervised machine learning modeling, the control system comprising:
- an artificial intelligence server including at least one processor;
- a data storage system to store historic time-series data for independent variables and a dependent variable, and a time series forecasting model;
- and
- an unsupervised machine learning modeling system including machine readable instructions stored on a non-transitory computer readable medium and executed by the artificial intelligence server to:
- build the time series forecasting model from the stored historic time-series data according to an unsupervised machine learning function, wherein the time series forecasting model includes the independent variables from the stored historic time-series data and the dependent variable from the stored historic time-series data, and wherein the building of the time series forecasting model comprises:
- determining a time-varying parameter (β) for each of the independent variables and for continuous consecutive time intervals of the stored historic time-series data;
- restricting a variance of at least one of the β's if overfitting for the time series forecasting model is estimated to occur;
- and
- for each of the independent variables, multiplying the independent variable with the time-varying parameter for the independent variable;
- and
- apply the time series forecasting model to a data set, wherein the applying of the time series forecasting model includes:
- determining whether the time series forecasting model is to be applied for making predictions for higher granularity time intervals that are larger than the time intervals of the stored time-series data used to build the model;
- if the predictions are for the higher granularity time intervals, modifying the β's for the independent variables to be for higher granularity time intervals;
- estimating a contribution of each of the independent variables for each of the consecutive time periods to the dependent variable;
- and
- controlling at least one device according to the estimated contributions of the independent variables.
13 more elements
A non-transitory computer readable medium comprising machine readable instructions executable by at least one processor to:
- determine an unsupervised machine learning function;
- build a time series forecasting model from historic time-series data according to the unsupervised machine learning function, wherein the time series forecasting model includes independent variables from the stored historic time-series data and a dependent variable from the stored historic time-series data, and wherein to build the time series forecasting model, the at least one processor is to:
- determine a time-varying parameter (β) for each of the independent variables and for continuous consecutive time intervals of the historic time-series data;
- restrict a variance of at least one of the β's if overfitting for the time series forecasting model is estimated to occur;
- and
- for each of the independent variables, multiply the independent variable with the time-varying parameter for the independent variable;
- and
- apply the time series forecasting model to a data set, wherein the applying of the time series forecasting model includes:
- estimating a contribution of each of the independent variables for each of the consecutive time periods to the dependent variable;
- and
- controlling at least one device according to the estimated contributions of the independent variables.
8 more elements
A non-transitory computer readable medium comprising machine readable instructions executable by at least one processor to:
- determine an unsupervised machine learning function;
- and
- build a time series forecasting model from historic time-series data according to the unsupervised machine learning function, wherein the time series forecasting model includes independent variables from the stored historic time-series data and a dependent variable from the stored historic time-series data, and wherein to build the time series forecasting model, the at least one processor is to:
- determine a time-varying parameter (β) for each of the independent variables and for continuous consecutive time intervals of the historic time-series data;
- restrict a variance of at least one of the β's if overfitting for the time series forecasting model is estimated to occur;
- and
- for each of the independent variables, multiply the independent variable with the time-varying parameter for the independent variable;
- and
- apply the time series forecasting model to a data set, wherein to apply the time series forecasting model the at least one processor is to:
- estimate a contribution of each of the independent variables for each of the consecutive time periods to the dependent variable;
- determine whether the time series forecasting model is to be applied for making predictions for higher granularity time intervals that are larger than the time intervals of the stored time-series data used to build the model;
- if the predictions are for the higher granularity time intervals, modify the β's for the independent variables to be for higher granularity time intervals;
- and
- control at least one device according to the estimated contributions of the independent variables.
11 more elements
Dependencies are inferred from the claim text.