Files
QuanTAlib/lib/errors/Mpe.cs
T
2024-10-27 16:11:08 -07:00

120 lines
4.0 KiB
C#

using System.Runtime.CompilerServices;
namespace QuanTAlib;
/// <summary>
/// MPE: Mean Percentage Error
/// A percentage-based error metric that measures the average percentage difference
/// between actual and predicted values. Like ME, it allows positive and negative
/// errors to cancel out, but expresses the bias in percentage terms.
/// </summary>
/// <remarks>
/// The MPE calculation process:
/// 1. Calculates percentage error for each point
/// 2. Sums all percentage errors (allowing cancellation)
/// 3. Divides by the number of observations
///
/// Key characteristics:
/// - Scale-independent (percentage-based)
/// - Can detect systematic bias
/// - Positive MPE indicates underprediction
/// - Negative MPE indicates overprediction
/// - Cannot handle zero actual values
/// - Errors can cancel out
///
/// Formula:
/// MPE = (1/n) * Σ((actual - predicted) / actual) * 100%
///
/// Sources:
/// https://en.wikipedia.org/wiki/Mean_percentage_error
/// https://www.statisticshowto.com/mean-percentage-error/
///
/// Note: Similar to MAPE but allows error cancellation
/// </remarks>
[SkipLocalsInit]
public sealed class Mpe : AbstractBase
{
private readonly CircularBuffer _actualBuffer;
private readonly CircularBuffer _predictedBuffer;
/// <param name="period">The number of points over which to calculate the MPE.</param>
/// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 1.</exception>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public Mpe(int period)
{
if (period < 1)
{
throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1.");
}
WarmupPeriod = period;
_actualBuffer = new CircularBuffer(period);
_predictedBuffer = new CircularBuffer(period);
Name = $"Mpe(period={period})";
Init();
}
/// <param name="source">The data source object that publishes updates.</param>
/// <param name="period">The number of points over which to calculate the MPE.</param>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public Mpe(object source, int period) : this(period)
{
var pubEvent = source.GetType().GetEvent("Pub");
pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public override void Init()
{
base.Init();
_actualBuffer.Clear();
_predictedBuffer.Clear();
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
protected override void ManageState(bool isNew)
{
if (isNew)
{
_lastValidValue = Input.Value;
_index++;
}
}
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
private static double CalculatePercentageError(double actual, double predicted)
{
return actual != 0 ? (actual - predicted) / actual : 0;
}
[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
protected override double Calculation()
{
ManageState(Input.IsNew);
double actual = Input.Value;
_actualBuffer.Add(actual, Input.IsNew);
// If no predicted value provided, use mean of actual values
double predicted = double.IsNaN(Input2.Value) ? _actualBuffer.Average() : Input2.Value;
_predictedBuffer.Add(predicted, Input.IsNew);
double mpe = 0;
if (_actualBuffer.Count > 0)
{
ReadOnlySpan<double> actualValues = _actualBuffer.GetSpan();
ReadOnlySpan<double> predictedValues = _predictedBuffer.GetSpan();
double sumPercentageError = 0;
for (int i = 0; i < actualValues.Length; i++)
{
sumPercentageError += CalculatePercentageError(actualValues[i], predictedValues[i]);
}
mpe = sumPercentageError / actualValues.Length;
}
IsHot = _index >= WarmupPeriod;
return mpe;
}
}