Asp.net 时间序列预测ML.Net

进入asp.net 3/4年后，我从几周开始尝试学习ML.net，但我在预测时间序列方面遇到了一些问题

以下是我的主要问题：

• 我想预测未来7天的价格，但我的数据分布不均匀，我如何才能迫使地平线预测未来7天的价格，而不仅仅是7次
• 我可以预测同一发动机中的所有3个值（单价、价格包10、价格包100）吗
• 每个项目都有自己的“寿命”（一个项目的价格可能下降，而另一个项目的价格可能上升），我需要为每个项目创建一个模型吗
• 即使我的数据集目前还不够大，我怎样才能从这些数据中得到更好的预测（文章末尾的预测结果）

我的最终目标是向我的ML core发送一个带有当前价格（单位、包装10、包装100）的项目名称，然后core预测一个最佳销售价格和日期。然后用简单的数学，我可以选择买或不买

以下是我构建时间序列预测模型的全部过程

首先，加载数据

        string queryThisWeek = "SELECT * from Ressource where ressource ='" + ressourceToHandle.Ressource + "' and Date >= '" + startCurrentWeek + "' order by Date desc";
        string queryBeforeThisWeek = "SELECT * from Ressource where ressource ='" + ressourceToHandle.Ressource + "' and Date < '" + startCurrentWeek + "' order by Date desc";
        var connection = new SqliteConnection(ConnectionString);
        var factory = DbProviderFactories.GetFactory(connection);
        DatabaseSource dbSourceThisWeek = new DatabaseSource(factory, ConnectionString, queryThisWeek);
        DatabaseSource dbSourceBeforeThisWeek = new DatabaseSource(factory, ConnectionString, queryBeforeThisWeek);
        IDataView dataViewThisWeek = loader.Load(dbSourceThisWeek);
        IDataView dataViewBeforeThisWeek = loader.Load(dbSourceBeforeThisWeek);

我的设置是否符合我的需要

评估我对每个价格的3个预测：

        actual = mlContext.Data.CreateEnumerable<Ressources>(testData, true)
                            .Select(observed => observed.Prix1);
        forecast = mlContext.Data.CreateEnumerable<RessourceLot1Prediction>(predictions, true)
                .Select(prediction => prediction.PriceForecasted[0]);

        // Calculate error (actual - forecast)
        var metrics = actual.Zip(forecast, (actualValue, forecastValue) => actualValue - forecastValue);

        // Get metric averages
        var MAE = metrics.Average(error => Math.Abs(error)); // Mean Absolute Error
        var RMSE = Math.Sqrt(metrics.Average(error => Math.Pow(error, 2))); // Root Mean Squared Error

        actual = mlContext.Data.CreateEnumerable<Ressources>(testData, true)
                            .Select(observed => observed.Prix1);
        forecast = mlContext.Data.CreateEnumerable<RessourceLot1Prediction>(predictions, true)
                .Select(prediction => prediction.PriceForecasted[0]);

        // Calculate error (actual - forecast)
        var metrics = actual.Zip(forecast, (actualValue, forecastValue) => actualValue - forecastValue);

        // Get metric averages
        var MAE = metrics.Average(error => Math.Abs(error)); // Mean Absolute Error
        var RMSE = Math.Sqrt(metrics.Average(error => Math.Pow(error, 2))); // Root Mean Squared Error

  var forecastEngineLot1 = forecaster.CreateTimeSeriesEngine<Ressources, RessourceLot1Prediction>(core.mlContext);
  forecastEngineLot1.CheckPoint(core.mlContext, modelPath);

            IEnumerable<string> forecastOutput =
            mlContext.Data.CreateEnumerable<Ressources>(testData, reuseRowObject: false)
                .Take(horizon)
                .Select((Ressources ress, int index) =>
                {
                    string Date = ress.Date.ToString("dd-MM-yyyy hh:mm");
                    float actualPrice = ress.Prix1;
                    float lowerEstimate = Math.Max(0, forecast.LowerBoundPrice[index]);
                    float estimate = forecast.PriceForecasted[index];
                    float upperEstimate = forecast.UpperBoundPrice[index];
                    return $"Date: {Date}\n" +
                    $"Actual price: {actualPrice}\n" +
                    $"Lower Estimate: {lowerEstimate}\n" +
                    $"Forecast: {estimate}\n" +
                    $"Upper Estimate: {upperEstimate}\n";
                });

public class Ressources
{
    [LoadColumn(0)]
    public string Ressource { get; set; }
    [LoadColumn(1)]
    public float Price1 { get; set; }
    [LoadColumn(2)]
    public float Price10 { get; set; }
    [LoadColumn(3)]
    public float Price100 { get; set; }
    [LoadColumn(4)]
    public float PriceAverage { get; set; }
    [LoadColumn(5)]
    public DateTime Date { get; set; }
}

public class RessourceLot1Prediction
{
    public float[] PriceForecasted { get; set; }

    public float[] LowerBoundPrice { get; set; }

    public float[] UpperBoundPrice { get; set; }
}

public class RessourceLot10Prediction
{
    public float[] PriceForecasted { get; set; }

    public float[] LowerBoundPrice { get; set; }

    public float[] UpperBoundPrice { get; set; }
}

public class RessourceLot100Prediction
{
    public float[] PriceForecasted { get; set; }

    public float[] LowerBoundPrice { get; set; }

    public float[] UpperBoundPrice { get; set; }
}