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Miha Kralj
2024-10-13 17:18:31 -07:00
parent 2236f5f483
commit bbefc72d73
65 changed files with 669 additions and 1087 deletions
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lib/errors/Mase.cs
+59 -69
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@@ -1,45 +1,40 @@
using System;
namespace QuanTAlib;
/// <summary>
/// Represents a Mean Absolute Scaled Error calculator that measures the ratio of the mean absolute error
/// of the forecast values to the mean absolute error of the naive forecast.
/// Represents the Mean Absolute Scaled Error (MASE) calculation.
/// </summary>
/// <remarks>
/// The Mase class calculates the Mean Absolute Scaled Error using circular buffers
/// to efficiently manage the data points within the specified period.
/// </remarks>
public class Mase : AbstractBase
{
private readonly CircularBuffer _actualBuffer;
private readonly CircularBuffer _forecastBuffer;
private readonly int _period;
private readonly CircularBuffer _predictedBuffer;
private readonly CircularBuffer _naiveBuffer;
/// <summary>
/// Initializes a new instance of the Mase class with the specified period.
/// Initializes a new instance of the Mase class.
/// </summary>
/// <param name="period">The period over which to calculate the Mean Absolute Scaled Error.</param>
/// <exception cref="ArgumentOutOfRangeException">
/// Thrown when period is less than 3.
/// </exception>
/// <param name="period">The period for MASE calculation.</param>
/// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 1.</exception>
public Mase(int period)
{
if (period < 3)
if (period < 1)
{
throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 3.");
throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1.");
}
_period = period;
WarmupPeriod = period;
_actualBuffer = new CircularBuffer(period);
_forecastBuffer = new CircularBuffer(period);
_predictedBuffer = new CircularBuffer(period);
_naiveBuffer = new CircularBuffer(period);
Name = $"Mase(period={period})";
Init();
}
/// <summary>
/// Initializes a new instance of the Mase class with the specified source and period.
/// Initializes a new instance of the Mase class with a source object.
/// </summary>
/// <param name="source">The source object to subscribe to for value updates.</param>
/// <param name="period">The period over which to calculate the Mean Absolute Scaled Error.</param>
/// <param name="source">The source object for event subscription.</param>
/// <param name="period">The period for MASE calculation.</param>
public Mase(object source, int period) : this(period)
{
var pubEvent = source.GetType().GetEvent("Pub");
@@ -47,19 +42,20 @@ public class Mase : AbstractBase
}
/// <summary>
/// Initializes the Mase instance by clearing the buffers.
/// Initializes the Mase instance.
/// </summary>
public override void Init()
{
base.Init();
_actualBuffer.Clear();
_forecastBuffer.Clear();
_predictedBuffer.Clear();
_naiveBuffer.Clear();
}
/// <summary>
/// Manages the state of the Mase instance based on whether new values are being processed.
/// Manages the state of the Mase instance.
/// </summary>
/// <param name="isNew">Indicates whether the current inputs are new values.</param>
/// <param name="isNew">Indicates if the input is new.</param>
protected override void ManageState(bool isNew)
{
if (isNew)
@@ -70,17 +66,9 @@ public class Mase : AbstractBase
}
/// <summary>
/// Performs the Mean Absolute Scaled Error calculation for the current period.
/// Performs the MASE calculation.
/// </summary>
/// <returns>
/// The calculated Mean Absolute Scaled Error value for the current period.
/// </returns>
/// <remarks>
/// This method calculates the Mean Absolute Scaled Error using the formula:
/// MASE = mean(|actual - forecast|) / mean(|actual[t] - actual[t-1]|)
/// where actual is each actual value and forecast is each forecast value.
/// If there are fewer than 3 values in the buffers, the method returns 0.
/// </remarks>
/// <returns>The calculated MASE value.</returns>
protected override double Calculation()
{
ManageState(Input.IsNew);
@@ -88,49 +76,51 @@ public class Mase : AbstractBase
double actual = Input.Value;
_actualBuffer.Add(actual, Input.IsNew);
double forecast = double.IsNaN(Input2.Value) ? _actualBuffer.Average() : Input2.Value;
_forecastBuffer.Add(forecast, Input.IsNew);
double predicted = double.IsNaN(Input2.Value) ? _actualBuffer.Average() : Input2.Value;
_predictedBuffer.Add(predicted, Input.IsNew);
double mase = 0;
if (_actualBuffer.Count >= 3)
if (_actualBuffer.Count > 1)
{
var actualValues = _actualBuffer.GetSpan().ToArray();
var forecastValues = _forecastBuffer.GetSpan().ToArray();
double sumAbsoluteError = 0;
double sumAbsoluteNaiveError = 0;
int count = Math.Min(_actualBuffer.Count, _period);
for (int i = 1; i < count; i++)
{
sumAbsoluteError += Math.Abs(actualValues[i] - forecastValues[i]);
sumAbsoluteNaiveError += Math.Abs(actualValues[i] - actualValues[i - 1]);
}
double meanAbsoluteError = sumAbsoluteError / (count - 1);
double meanAbsoluteNaiveError = sumAbsoluteNaiveError / (count - 1);
if (meanAbsoluteNaiveError != 0)
{
mase = meanAbsoluteError / meanAbsoluteNaiveError;
}
_naiveBuffer.Add(_actualBuffer.GetSpan()[^2], Input.IsNew);
}
double mase = CalculateMase();
IsHot = _index >= WarmupPeriod;
return mase;
}
/// <summary>
/// Calculates the Mean Absolute Scaled Error for the given actual and forecast values.
/// </summary>
/// <param name="actual">The actual value.</param>
/// <param name="forecast">The forecast value.</param>
/// <returns>The calculated Mean Absolute Scaled Error.</returns>
public double Calc(double actual, double forecast)
private double CalculateMase()
{
Input = new TValue(DateTime.Now, actual);
Input2 = new TValue(DateTime.Now, forecast);
return Calculation();
if (_actualBuffer.Count <= 1) return 0;
ReadOnlySpan<double> actualValues = _actualBuffer.GetSpan();
ReadOnlySpan<double> predictedValues = _predictedBuffer.GetSpan();
ReadOnlySpan<double> naiveValues = _naiveBuffer.GetSpan();
double sumAbsoluteError = CalculateSumAbsoluteError(actualValues, predictedValues);
double _naiveForecastError = CalculateNaiveForecastError(actualValues, naiveValues);
return _naiveForecastError != 0 ? (sumAbsoluteError / _actualBuffer.Count) / _naiveForecastError : double.PositiveInfinity;
}
private static double CalculateSumAbsoluteError(ReadOnlySpan<double> actualValues, ReadOnlySpan<double> predictedValues)
{
double sum = 0;
for (int i = 0; i < actualValues.Length; i++)
{
sum += Math.Abs(actualValues[i] - predictedValues[i]);
}
return sum;
}
private static double CalculateNaiveForecastError(ReadOnlySpan<double> actualValues, ReadOnlySpan<double> naiveValues)
{
double sum = 0;
for (int i = 1; i < actualValues.Length; i++)
{
sum += Math.Abs(actualValues[i] - naiveValues[i - 1]);
}
return sum / (actualValues.Length - 1);
}
}