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US 11,775,996 B2Granted
Systems and methods for features engineering
AssigneeKinaxis Inc.
InventorsSebastien Ouellet, Zhen Lin, Christopher Wang, Chantal Bisson-Krol
Life of this patent
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Abstract
Systems and methods for features engineering, in which internal and external signals are received and fused. The fusing is based on meta-data of each of the one or more internal signals and each of the one or more external signals. A set of features is generated based on one or more valid combinations that match a transformation input, the transformation forming part of library of transformations. Finally, a set of one or more features is selected from the plurality of features, based on a predictive strength of each feature. The set of selected features can be used to train and select a machine learning model.
Claims24
A computer-implemented method comprising:
- engineering, by a processor, one or more features;
- and
- choosing, by the processor, a forecasting method to make a forecast, based on a forecast request;
- wherein engineering one or more features comprises:
- receiving, by the processor, internal signal data;
- receiving, by the processor, external signal data;
- fusing, by the processor, data from the internal signal data and the external signal data, the fusing based on meta-data of each of the internal signal data and each of the external signal data;
- generating, by the processor, a plurality of features based on one or more valid combinations that match a transformation input, the transformation forming part of a library of transformations;
- and
- selecting, by the processor, one or more features from the plurality of features, based on a predictive strength of each feature, to provide a set of selected features;
- and wherein choosing the forecasting method comprises:
- training, by the processor, one or more machine learning models, using the set of selected features;
- and either:
- i) selecting, by the processor, a machine learning model from the one or more machine learning models to make the forecast;
- or
- ii) retraining, by the processor, a previously-selected machine learning model from the one or more machine learning models and using the retrained model to make the forecast;
- or
- iii) making, by the processor, the forecast based on the previously-selected machine learning model.
15 more elements
A system comprising:
- a processor;
- and
- a memory storing instructions that, when executed by the processor, configure the system to:
- engineer, by the processor, one or more features;
- and
- choose, by the processor, a forecasting method to make a forecast, based on a forecast request;
- wherein when engineering the one or more features, the system is configured to:
- receive, by the processor, internal signal data;
- receive, by the processor, external signal data;
- fuse, by the processor, data from the internal signal data and the external signal data, the fusing based on meta-data of each of the internal signal data and each of the external signal data;
- generate, by the processor, a plurality of features based on one or more valid combinations that match a transformation input, the transformation forming part of a library of transformations;
- and
- select, by the processor, one or more features from the plurality of features, based on a predictive strength of each feature, to provide a set of selected features;
- and wherein when choosing the forecasting method, the system is configured to:
- train, by the processor, one or more machine learning models, using the set of selected features;
- and either:
- i) select, by the processor, a machine learning model from the one or more machine learning models to make the forecast;
- or
- ii) retrain, by the processor, a previously-selected machine learning model from the one or more machine learning models and using the retrained model to make the forecast;
- or
- iii) make, by the processor, the forecast based on the previously-selected machine learning model.
18 more elements
A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:
- engineer, by a processor, one or more features;
- and
- choose, by the processor, a forecasting method to make a forecast, based on a forecast request;
- wherein when engineering one or more features, the instructions that when executed by the computer, cause the computer to:
- receive, by the processor, internal signal data;
- receive, by the processor, external signal data;
- fuse, by the processor, data from the internal signal data and the external signal data, the fusing based on meta-data of each of the internal signal data and each of the external signal data;
- generate, by the processor, a plurality of features based on one or more valid combinations that match a transformation input, the transformation forming part of a library of transformations;
- and
- select, by the processor, one or more features from the plurality of features, based on a predictive strength of each feature, to provide a set of selected features;
- and wherein when choosing the forecasting method, the instructions that when executed by the computer, cause the computer to:
- train, by the processor, one or more machine learning models, using the set of selected features;
- and either:
- i) select, by the processor, a machine learning model from the one or more machine learning models to make the forecast;
- or
- ii) retrain, by the processor, a previously-selected machine learning model from the one or more machine learning models and using the retrained model to make the forecast;
- or
- iii) make, by the processor, the forecast based on the previously-selected machine learning model.
15 more elements
Dependencies are inferred from the claim text.