MACHINE LEARNING MODEL SELECTION
Forecasting models are tested for accuracy metrics on a plurality of historical data sets for a plurality of businesses. An optimal forecasting model is determined for each business's historical data set. A forecasting selection or recommendation model is trained on each business's historical data set to predict the corresponding optimal forecasting model. When a given business desires an updated forecast, a most recent historical data set is obtained and provided as input to the recommendation model. The recommendation model returns as output a predicted optimal forecasting model. The optimal forecasting model is processed with the most recent historical data set to obtain a forecast and the forecast is provided to the business.
1 . A method, comprising:
testing forecasting machine learning models (models) for accuracy in providing forecasts based on a plurality of historical data sets, each historical data set associated with a business;
determining an optimal forecasting model for each historical data set based on the testing;
training a recommendation model to predict the optimal forecasting model for each historical data set; and
processing the recommendation model to predict subsequent optimal forecasting models for subsequent and most recent historical data sets of the businesses.
2 . The method of claim 1 further comprising, processing the subsequent optimal forecasting models with the subsequent and most recent historical data sets to obtain subsequent forecasts, and providing the subsequent forecasts to the businesses.
3 . The method of claim 1 , wherein testing further includes providing each historical data set to the forecasting models in parallel and obtaining candidate forecasts as outputs from the forecasting models for each business.
4 . The method of claim 3 , wherein testing further includes calculating accuracy metrics from the candidate forecasts of each business.
5 . The method of claim 4 , wherein determining further includes determining the optimal forecasting model for each business based on corresponding accuracy metrics.
6 . The method of claim 1 , wherein training further includes normalizing each historical data set into a two-dimensional (2D) set of time series data.
7 . The method of claim 6 , wherein normalizing further includes generating a training record for each historical data set comprising a pointer to a corresponding 2D set of time series data and an identifier for a corresponding optimal forecasting model.
8 . The method of claim 7 , wherein generating further includes segmenting a first portion of the training records for training and a second portion of training records for testing an accuracy of the recommendation model.
9 . The method of claim 8 , wherein training further includes training the recommendation model on the first portion of the training records using a 2D convolutional neural network (CNN) deep learning algorithm to learn from the 2D sets of time series data.
10 . A method, comprising:
obtaining a historical time series data set associated with a forecast;
processing a recommendation machine learning model (model) using the historical time series data to obtain an identifier for an optimal forecasting model to provide the forecast;
processing the forecasting model based on the identifier with the historical time series data to obtain the forecast; and
provide the forecast to a system associated with the historical time series data set.
11 . The method of claim 10 , wherein obtaining further includes obtaining the historical time series data set based on a request received from a requestor.
12 . The method of claim 10 , wherein obtaining further includes obtaining the historical time series data set based on a configured interval of elapsed time.
13 . The method of claim 10 , wherein processing the recommendation model further includes normalizing the historical time series data into a two-dimensional (2D) time series data set of data and providing the 2D time series set of data as input to the recommendation model.
14 . The method of claim 10 , wherein processing the forecasting model further includes using the identifier to select the forecasting model from a plurality of available forecasting models.
15 . The method of claim 10 , wherein providing further includes providing the forecast to the system via an application programming interface.
16 . The method of claim 10 further comprising:
iterating to the obtaining at a preconfigured interval of time and updating the historical time series data as most recent historical time series data.
17 . The method of claim 10 further comprising:
processing the method as a cloud-based service to the system.
18 . The method of claim 10 further comprising:
maintaining the recommendation model as a convolutional neural network (CNN) model.
19 . A system comprising:
a cloud comprising a plurality of servers;
each server comprising at least one processor and a non-transitory computer-readable storage medium;
each non-transitory computer-readable storage medium comprising executable instructions;
the executable instructions when provided to or obtained by a corresponding processor cause the corresponding processor to perform operations, comprising:
training a recommendation machine learning model (model) to provide a predicted optimal forecasting model based on characteristics in a historical data set used as input to a plurality of available forecasting models;
obtaining a most recent historical data set associated with a request to obtain a forecast;
processing the recommendation model using the most recent historical data set and obtaining a currently predicted optimal forecasting model as output from the recommendation model;
processing the currently predicted optimal forecasting model using the most recent historical data set and obtaining a current forecast as output from the currently predicted optimal forecasting model; and
providing the current forecast.
20 . The system of claim 19 , wherein the forecast is a sales forecast for a business and provides sales for the business predicted at a configured interval of time over a future period of time.