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[ml] 가격예측추가
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Lines changed: 367 additions & 29 deletions

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‎ActorLib/ActorLib.csproj‎

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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<TargetFramework>net9.0</TargetFramework>
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<ImplicitUsings>enable</ImplicitUsings>
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<AkkaVersion>1.5.40</AkkaVersion>
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<AkkaPersistenceVersion>1.5.37</AkkaPersistenceVersion>
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<AkkaMinVersion>1.5.13</AkkaMinVersion>
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<Nullable>enable</Nullable>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Akka" Version="$(AkkaVersion)" />
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<PackageReference Include="Akka.Remote" Version="$(AkkaVersion)" />
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<PackageReference Include="Akka.Streams" Version="$(AkkaVersion)" />
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<PackageReference Include="Akka.DependencyInjection" Version="$(AkkaVersion)" />
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<PackageReference Include="Akka.Logger.NLog" Version="$(AkkaMinVersion)" />
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<PackageReference Include="Akka.Persistence.RavenDB" Version="$(AkkaPersistenceVersion)" />
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<PackageReference Include="Akka.Persistence.RavenDB.Hosting" Version="$(AkkaPersistenceVersion)" />
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<PackageReference Include="Flurl.Http" Version="4.0.2" />
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<PackageReference Include="ModelContextProtocol" Version="0.1.0-preview.9" />
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<PackageReference Include="NLog" Version="5.4.0" />
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<PackageReference Include="RavenDB.Client" Version="7.0.1" />
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</ItemGroup>
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<ItemGroup>
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<Folder Include="MCP\" />
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</ItemGroup>
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</Project>
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<PropertyGroup>
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<TargetFramework>net9.0</TargetFramework>
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<ImplicitUsings>enable</ImplicitUsings>
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<AkkaVersion>1.5.40</AkkaVersion>
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<AkkaPersistenceVersion>1.5.37</AkkaPersistenceVersion>
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<AkkaMinVersion>1.5.13</AkkaMinVersion>
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<Nullable>enable</Nullable>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Akka" Version="$(AkkaVersion)" />
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<PackageReference Include="Akka.Remote" Version="$(AkkaVersion)" />
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<PackageReference Include="Akka.Streams" Version="$(AkkaVersion)" />
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<PackageReference Include="Akka.DependencyInjection" Version="$(AkkaVersion)" />
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<PackageReference Include="Akka.Logger.NLog" Version="$(AkkaMinVersion)" />
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<PackageReference Include="Akka.Persistence.RavenDB" Version="$(AkkaPersistenceVersion)" />
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<PackageReference Include="Akka.Persistence.RavenDB.Hosting" Version="$(AkkaPersistenceVersion)" />
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<PackageReference Include="Flurl.Http" Version="4.0.2" />
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<PackageReference Include="Microsoft.ML.TimeSeries" Version="3.0.1" />
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<PackageReference Include="ModelContextProtocol" Version="0.1.0-preview.9" />
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<PackageReference Include="NLog" Version="5.4.0" />
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<PackageReference Include="RavenDB.Client" Version="7.0.1" />
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</ItemGroup>
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<ItemGroup>
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<Folder Include="MCP\" />
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</ItemGroup>
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<ItemGroup Label="MLPricePredictionModel">
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<None Include="MLPricePredictionModel.mlnet">
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<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
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</None>
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</ItemGroup>
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</Project>

‎ActorLib/MLPricePredictionModel.consumption.cs‎

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// This file was auto-generated by ML.NET Model Builder.
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using Microsoft.ML;
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using Microsoft.ML.Data;
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using System;
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using System.Linq;
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using System.IO;
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using System.Collections.Generic;
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using Microsoft.ML.Transforms.TimeSeries;
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namespace ActorLib
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{
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public partial class MLPricePredictionModel
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{
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/// <summary>
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/// model input class for MLPricePredictionModel.
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/// </summary>
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#region model input class
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public class ModelInput
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{
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[LoadColumn(1)]
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[ColumnName(@"Price")]
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public float Price { get; set; }
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}
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#endregion
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/// <summary>
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/// model output class for MLPricePredictionModel.
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/// </summary>
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#region model output class
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public class ModelOutput
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{
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[ColumnName(@"Price")]
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public float[] Price { get; set; }
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[ColumnName(@"Price_LB")]
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public float[] Price_LB { get; set; }
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[ColumnName(@"Price_UB")]
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public float[] Price_UB { get; set; }
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}
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#endregion
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private static string MLNetModelPath = Path.GetFullPath(@"D:\Code\Webnori\NetCoreLabs\ActorLib\MLPricePredictionModel.mlnet");
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public static readonly Lazy<TimeSeriesPredictionEngine<ModelInput, ModelOutput>> PredictEngine = new Lazy<TimeSeriesPredictionEngine<ModelInput, ModelOutput>>(() => CreatePredictEngine(), true);
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/// <summary>
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/// Use this method to predict on <see cref="ModelInput"/>.
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/// </summary>
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/// <param name="input">model input.</param>
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/// <returns><seealso cref=" ModelOutput"/></returns>
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public static ModelOutput Predict(ModelInput? input = null, int? horizon = null)
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{
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var predEngine = PredictEngine.Value;
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return predEngine.Predict(input, horizon);
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}
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private static TimeSeriesPredictionEngine<ModelInput, ModelOutput> CreatePredictEngine()
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{
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var mlContext = new MLContext();
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ITransformer mlModel = mlContext.Model.Load(MLNetModelPath, out var schema);
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return mlModel.CreateTimeSeriesEngine<ModelInput, ModelOutput>(mlContext);
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}
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}
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}
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‎ActorLib/MLPricePredictionModel.mbconfig‎

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{
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"Scenario": "Forecasting",
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"DataSource": {
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"Version": 3,
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"EscapeCharacter": "\"",
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"ReadMultiLines": false,
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"KeepDiacritics": false,
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"KeepPunctuations": false,
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"AllowQuoting": false,
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"Type": "TabularFile",
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"FilePath": "D:\\BI\\가격예측\\전체월별매출.CSV",
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"Delimiter": ",",
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"DecimalMarker": ".",
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"HasHeader": true,
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"ColumnProperties": [
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{
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"ColumnName": "Date",
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"ColumnPurpose": "Feature",
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"ColumnDataFormat": "String",
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"IsCategorical": false,
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"Type": "Column",
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"Version": 5
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},
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{
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"ColumnName": "Price",
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"ColumnPurpose": "Feature",
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"ColumnDataFormat": "Single",
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"IsCategorical": false,
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"Type": "Column",
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"Version": 5
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}
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]
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},
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"Environment": {
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"Type": "LocalCPU",
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"Version": 1
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},
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"RunHistory": {
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"Version": 3,
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"Type": "Result",
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"Trials": [
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{
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"Version": 1,
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"Type": "Trial",
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"TrainerName": "ForecastBySsa",
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"Score": 13626.667831355951,
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"RuntimeInSeconds": 0.056,
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"Parameter": {
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"_SCHEMA_": "e0",
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"e0": {
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"WindowSize": 2,
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"SeriesLength": 10,
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"TrainSize": 17,
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"Horizon": 20,
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"OutputColumnName": "Price",
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"InputColumnName": "Price",
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"ConfidenceLowerBoundColumn": "Price_LB",
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"ConfidenceUpperBoundColumn": "Price_UB"
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}
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}
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},
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{
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"Version": 1,
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"Type": "Trial",
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"TrainerName": "ForecastBySsa",
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"Score": 13626.667831355951,
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"RuntimeInSeconds": 0.002,
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"Parameter": {
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"_SCHEMA_": "e0",
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"e0": {
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"WindowSize": 2,
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"SeriesLength": 2712,
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"TrainSize": 17,
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"Horizon": 20,
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"OutputColumnName": "Price",
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"InputColumnName": "Price",
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"ConfidenceLowerBoundColumn": "Price_LB",
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"ConfidenceUpperBoundColumn": "Price_UB"
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}
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}
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},
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{
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"Version": 1,
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"Type": "Trial",
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"TrainerName": "ForecastBySsa",
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"Score": 13626.667831355951,
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"RuntimeInSeconds": 0.003,
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"Parameter": {
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"_SCHEMA_": "e0",
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"e0": {
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"WindowSize": 2,
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"SeriesLength": 978,
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"TrainSize": 17,
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"Horizon": 20,
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"OutputColumnName": "Price",
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"InputColumnName": "Price",
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"ConfidenceLowerBoundColumn": "Price_LB",
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"ConfidenceUpperBoundColumn": "Price_UB"
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}
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}
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},
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{
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"Version": 1,
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"Type": "Trial",
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"TrainerName": "ForecastBySsa",
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"Score": 13626.667831355951,
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"RuntimeInSeconds": 0.002,
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"Parameter": {
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"_SCHEMA_": "e0",
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"e0": {
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"WindowSize": 2,
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"SeriesLength": 437,
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"TrainSize": 17,
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"Horizon": 20,
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"OutputColumnName": "Price",
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"InputColumnName": "Price",
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"ConfidenceLowerBoundColumn": "Price_LB",
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"ConfidenceUpperBoundColumn": "Price_UB"
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}
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}
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},
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{
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"Version": 1,
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"Type": "Trial",
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"TrainerName": "ForecastBySsa",
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"Score": 13626.667831355951,
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"RuntimeInSeconds": 0.002,
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"Parameter": {
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"_SCHEMA_": "e0",
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"e0": {
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"WindowSize": 2,
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"SeriesLength": 31,
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"TrainSize": 17,
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"Horizon": 20,
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"OutputColumnName": "Price",
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"InputColumnName": "Price",
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"ConfidenceLowerBoundColumn": "Price_LB",
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"ConfidenceUpperBoundColumn": "Price_UB"
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}
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}
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}
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],
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"Estimators": {
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"e0": "ForecastBySsa"
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},
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"Schema": "e0",
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"MetricName": "RMSE",
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"ModelFilePath": "D:\\Code\\Webnori\\NetCoreLabs\\ActorLib\\MLPricePredictionModel.mlnet"
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},
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"Type": "TrainingConfig",
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"Version": 5,
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"TrainingOption": {
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"Version": 3,
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"OptimizeMetric": "RMSE",
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"Subsampling": false,
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"Tuner": "eci-cfo",
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"Type": "ForecastingTrainingOption",
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"TrainingTime": 10,
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"UseCrossValidation": false,
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"Horizon": 20,
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"TimeColumn": "Date",
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"LabelColumn": "Price",
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"UseDefaultIndex": false,
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"ValidationOption": {
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"Version": 0,
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"Type": "CrossValidationValidationOption",
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"NumberOfFolds": 5
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}
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}
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}

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