示例#1
0
        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);
        }