Systems and methods for features engineering

A computing apparatus for training one or more machine learning models for forecasting the demand for products at different locations, wherein the computing apparatus uses features engineering, and the computing apparatus comprises:

• a processor;
• and
• a memory storing instructions that, when executed by the processor, configure the apparatus to:
• receive, by an ingestion module, internal signal data comprising columns and values;
• receive, by the ingestion module, external signal data comprising columns and values;
• fuse 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 a plurality of features based on all of the combinations of columns that match a transformation inputs, the transformations forming part of a library of transformations;
• apply the combinations of columns and transformations to the fused data to produce feature generated data that can be used to train machine learning models;
• filter the feature generated data down to a smaller set of columns that have enhanced predictive power;
• select one or more features from the plurality of features, based on a predictive strength of each feature, to produce a set of selected features;
• and
• train the one or more machine learning models by using the set of selected features.

A non-transitory computer-readable storage medium for training one or more machine learning models for forecasting the demand for products at different locations, wherein the method uses features engineering and the computer-readable storage medium includes instructions that when executed by a computer, cause the computer to:

• receive, by an ingestion module, internal signal data comprising columns and values;
• receive, by the ingestion module, external signal data comprising columns and values;
• fuse 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 a plurality of features based on all of the combinations of columns that match transformation inputs, the transformations forming part of a library of transformations;
• apply the combinations of columns and transformations to the fused data to produce feature generated data that can be used to train machine learning models;
• filter the feature generated data down to a smaller set of columns that have enhanced predictive power;
• select one or more features from the plurality of features, based on a predictive strength of each feature, to produce a set of selected features;
• and
• train the one or more machine learning models by using the set of selected features.

A computer-implemented method for forecasting sales of a product at a location, the method comprising the steps of:

• engineering, by the processor, one or more features;
• and
• choosing, by the processor, a forecasting method, the forecast method based on a forecast request;
• wherein:
• engineering one or more features comprises:
• receiving, by the processor, internal signal data comprising columns and values;
• receiving, by the processor, external signal data comprising columns and values;
• 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 all of the combinations of columns that match transformation inputs, the transformations forming part of a library of transformations;
• applying, by the processor, the combinations of columns and transformations to the fused data to produce feature generated data that can be used to train machine learning models;
• filtering, by the processor, the feature generated data down to a smaller set of columns that have enhanced predictive power;
• and
• selecting, by the processor, one or more features from the plurality of features, based on a predictive strength of each feature, to produce 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 a machine learning model from the one or more machine learning models to make the forecast;
• or
• ii) retraining 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 the forecast based on the previously-selected machine learning model.