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https://github.com/mihakralj/QuanTAlib.git
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118 lines
4.1 KiB
C#
118 lines
4.1 KiB
C#
using System.Runtime.CompilerServices;
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namespace QuanTAlib;
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/// <summary>
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/// MAPE: Mean Absolute Percentage Error
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/// A percentage-based error metric that measures the average absolute percentage
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/// difference between predicted and actual values. MAPE expresses accuracy as a
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/// percentage, making it scale-independent and easy to interpret.
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/// </summary>
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/// <remarks>
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/// The MAPE calculation process:
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/// 1. Calculates absolute percentage error for each point
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/// 2. Sums all absolute percentage errors
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/// 3. Divides by the number of observations
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///
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/// Key characteristics:
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/// - Scale-independent (percentage-based)
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/// - Easy to interpret (0-100% range)
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/// - Useful for comparing different scales
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/// - Cannot handle zero actual values
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/// - Asymmetric (treats over/under predictions differently)
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///
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/// Formula:
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/// MAPE = (1/n) * Σ|((actual - predicted) / actual)| * 100%
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///
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/// Sources:
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/// https://en.wikipedia.org/wiki/Mean_absolute_percentage_error
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/// https://www.statisticshowto.com/mean-absolute-percentage-error-mape/
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///
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/// Note: Also known as MAPD (Mean Absolute Percentage Deviation) in some contexts
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/// </remarks>
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[SkipLocalsInit]
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public sealed class Mape : AbstractBase
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{
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private readonly CircularBuffer _actualBuffer;
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private readonly CircularBuffer _predictedBuffer;
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/// <param name="period">The number of points over which to calculate the MAPE.</param>
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/// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 1.</exception>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public Mape(int period)
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{
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if (period < 1)
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{
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throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1.");
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}
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WarmupPeriod = period;
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_actualBuffer = new CircularBuffer(period);
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_predictedBuffer = new CircularBuffer(period);
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Name = $"Mape(period={period})";
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Init();
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}
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/// <param name="source">The data source object that publishes updates.</param>
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/// <param name="period">The number of points over which to calculate the MAPE.</param>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public Mape(object source, int period) : this(period)
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{
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var pubEvent = source.GetType().GetEvent("Pub");
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pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public override void Init()
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{
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base.Init();
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_actualBuffer.Clear();
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_predictedBuffer.Clear();
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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protected override void ManageState(bool isNew)
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{
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if (isNew)
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{
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_lastValidValue = Input.Value;
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_index++;
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}
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
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private static double CalculatePercentageError(double actual, double predicted)
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{
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return actual != 0 ? Math.Abs((actual - predicted) / actual) : 0;
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
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protected override double Calculation()
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{
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ManageState(Input.IsNew);
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double actual = Input.Value;
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_actualBuffer.Add(actual, Input.IsNew);
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// If no predicted value provided, use mean of actual values
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double predicted = double.IsNaN(Input2.Value) ? _actualBuffer.Average() : Input2.Value;
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_predictedBuffer.Add(predicted, Input.IsNew);
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double mape = 0;
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if (_actualBuffer.Count > 0)
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{
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ReadOnlySpan<double> actualValues = _actualBuffer.GetSpan();
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ReadOnlySpan<double> predictedValues = _predictedBuffer.GetSpan();
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double sumAbsolutePercentageError = 0;
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for (int i = 0; i < actualValues.Length; i++)
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{
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sumAbsolutePercentageError += CalculatePercentageError(actualValues[i], predictedValues[i]);
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}
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mape = sumAbsolutePercentageError / actualValues.Length;
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}
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IsHot = _index >= WarmupPeriod;
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return mape;
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}
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}
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