public async Task <IActionResult> GetProductUnitDemandEstimation(string productId)
{
// Get product history.
var productHistory = await _queries.GetProductDataAsync(productId);
// Supplement the history with synthetic data.
var supplementedProductHistory = TimeSeriesDataGenerator.SupplementData(mlContext, productHistory);
var supplementedProductHistoryLength = supplementedProductHistory.Count(); // 36
var supplementedProductDataView = mlContext.Data.LoadFromEnumerable(supplementedProductHistory);
// Create and add the forecast estimator to the pipeline.
IEstimator <ITransformer> forecastEstimator = mlContext.Forecasting.ForecastBySsa(
outputColumnName: nameof(ProductUnitTimeSeriesPrediction.ForecastedProductUnits),
inputColumnName: nameof(ProductData.units), // This is the column being forecasted.
windowSize: 12, // Window size is set to the time period represented in the product data cycle; our product cycle is based on 12 months, so this is set to a factor of 12, e.g. 3.
seriesLength: supplementedProductHistoryLength, // TODO: Need clarification on what this should be set to; assuming product series length for now.
trainSize: supplementedProductHistoryLength, // TODO: Need clarification on what this should be set to; assuming product series length for now.
horizon: 1, // Indicates the number of values to forecast; 1 indicates that the next month of product units will be forecasted.
confidenceLevel: 0.95f, // TODO: Is this the same as prediction interval, where this indicates that we are 95% confidence that the forecasted value will fall within the interval range?
confidenceLowerBoundColumn: nameof(ProductUnitTimeSeriesPrediction.ConfidenceLowerBound), // TODO: See above comment.
confidenceUpperBoundColumn: nameof(ProductUnitTimeSeriesPrediction.ConfidenceUpperBound)); // TODO: See above comment.
// Train the forecasting model for the specified product's data series.
ITransformer forecastTransformer = forecastEstimator.Fit(supplementedProductDataView);
// Create the forecast engine used for creating predictions.
TimeSeriesPredictionEngine <ProductData, ProductUnitTimeSeriesPrediction> forecastEngine = forecastTransformer.CreateTimeSeriesEngine <ProductData, ProductUnitTimeSeriesPrediction>(mlContext);
// Predict.
var nextMonthUnitDemandEstimation = forecastEngine.Predict();
return(Ok(nextMonthUnitDemandEstimation.ForecastedProductUnits.First()));
}
/// <summary>
/// Build model for predicting next month's product unit sales using time series forecasting.
/// </summary>
/// <param name="mlContext">ML.NET context.</param>
/// <param name="productDataSeries">ML.NET IDataView representing the loaded product data series.</param>
/// <param name="outputModelPath">Trained model path.</param>
private static void TrainAndSaveModel(MLContext mlContext, IDataView productDataView, string outputModelPath)
{
ConsoleWriteHeader("Training product forecasting Time Series model");
var supplementedProductDataSeries = TimeSeriesDataGenerator.SupplementData(mlContext, productDataView);
var supplementedProductDataSeriesLength = supplementedProductDataSeries.Count(); // 36
var supplementedProductDataView = mlContext.Data.LoadFromEnumerable(supplementedProductDataSeries, productDataView.Schema);
// Create and add the forecast estimator to the pipeline.
IEstimator <ITransformer> forecastEstimator = mlContext.Forecasting.ForecastBySsa(
outputColumnName: nameof(ProductUnitTimeSeriesPrediction.ForecastedProductUnits),
inputColumnName: nameof(ProductData.units), // This is the column being forecasted.
windowSize: 12, // Window size is set to the time period represented in the product data cycle; our product cycle is based on 12 months, so this is set to a factor of 12, e.g. 3.
seriesLength: supplementedProductDataSeriesLength, // TODO: Need clarification on what this should be set to; assuming product series length for now.
trainSize: supplementedProductDataSeriesLength, // TODO: Need clarification on what this should be set to; assuming product series length for now.
horizon: 2, // Indicates the number of values to forecast; 2 indicates that the next 2 months of product units will be forecasted.
confidenceLevel: 0.95f, // TODO: Is this the same as prediction interval, where this indicates that we are 95% confidence that the forecasted value will fall within the interval range?
confidenceLowerBoundColumn: nameof(ProductUnitTimeSeriesPrediction.ConfidenceLowerBound), // TODO: See above comment.
confidenceUpperBoundColumn: nameof(ProductUnitTimeSeriesPrediction.ConfidenceUpperBound)); // TODO: See above comment.
// Train the forecasting model for the specified product's data series.
ITransformer forecastTransformer = forecastEstimator.Fit(supplementedProductDataView);
// Create the forecast engine used for creating predictions.
TimeSeriesPredictionEngine <ProductData, ProductUnitTimeSeriesPrediction> forecastEngine = forecastTransformer.CreateTimeSeriesEngine <ProductData, ProductUnitTimeSeriesPrediction>(mlContext);
// Save the forecasting model so that it can be loaded within an end-user app.
forecastEngine.CheckPoint(mlContext, outputModelPath);
}