TimeSeriesCatalog.DetectSeasonality 方法
定義
重要
部分資訊涉及發行前產品，在發行之前可能會有大幅修改。 Microsoft 對此處提供的資訊，不做任何明確或隱含的瑕疵擔保。
在時間序列資料中，季節性（或稱週期性）是指在特定規律間隔（如每週、每月或每季）出現變化的存在。
此方法透過採用傅立葉分析技術來偵測這個可預測的區間（或週期）。 假設輸入值的時間區間相同（例如，每秒收集的感測器資料依時間戳排序），此方法會接收時間序列資料清單，若能找到可預測的波動或模式，且在此期間內反覆出現或重複，則回傳輸入季節資料的正常週期。
若未發現此類模式，也就是輸入值不遵循季節性波動，則回 -1。
public static int DetectSeasonality(this Microsoft.ML.AnomalyDetectionCatalog catalog, Microsoft.ML.IDataView input, string inputColumnName, int seasonalityWindowSize = -1, double randomnessThreshold = 0.95);static member DetectSeasonality : Microsoft.ML.AnomalyDetectionCatalog * Microsoft.ML.IDataView * string * int * double -> int<Extension()>
Public Function DetectSeasonality (catalog As AnomalyDetectionCatalog, input As IDataView, inputColumnName As String, Optional seasonalityWindowSize As Integer = -1, Optional randomnessThreshold As Double = 0.95) As Integer參數
- catalog
- AnomalyDetectionCatalog
偵測季節性目錄。
- seasonalityWindowSize
- Int32
輸入值中應考慮數值的上限。 設為 -1 時，使用整個輸入來擬合模型;當設定為正整數時，僅考慮第一個 windowSize 數量的值。 預設值為 -1。
傳回
作為季節性資料輸入的規則間隔，否則回傳 -1。
範例
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.TimeSeries;
namespace Samples.Dynamic
{
public static class DetectSeasonality
{
public static void Example()
{
/* Create a new ML context, for ML.NET operations. It can be used for
exception tracking and logging, as well as the source of randomness.*/
var mlContext = new MLContext();
// Create a seasonal data as input: y = sin(2 * Pi + x)
var seasonalData = Enumerable.Range(0, 100).Select(x => new TimeSeriesData(Math.Sin(2 * Math.PI + x)));
// Load the input data as a DataView.
var dataView = mlContext.Data.LoadFromEnumerable(seasonalData);
/* Two option parameters:
* seasonalityWindowSize: Default value is -1. When set to -1, use the whole input to fit model;
* when set to a positive integer, only the first windowSize number of values will be considered.
* randomnessThreshold: Randomness threshold that specifies how confidence the input values follows
* a predictable pattern recurring as seasonal data. By default, it is set as 0.99.
* The higher the threshold is set, the more strict recurring pattern the
* input values should follow to be determined as seasonal data.
*/
int period = mlContext.AnomalyDetection.DetectSeasonality(
dataView,
nameof(TimeSeriesData.Value),
seasonalityWindowSize: 40);
// Print the Seasonality Period result.
Console.WriteLine($"Seasonality Period: #{period}");
}
private class TimeSeriesData
{
public double Value;
public TimeSeriesData(double value)
{
Value = value;
}
}
}
}