using System.Runtime.CompilerServices; namespace QuanTAlib; /// /// MAPE: Mean Absolute Percentage Error /// A percentage-based error metric that measures the average absolute percentage /// difference between predicted and actual values. MAPE expresses accuracy as a /// percentage, making it scale-independent and easy to interpret. /// /// /// The MAPE calculation process: /// 1. Calculates absolute percentage error for each point /// 2. Sums all absolute percentage errors /// 3. Divides by the number of observations /// /// Key characteristics: /// - Scale-independent (percentage-based) /// - Easy to interpret (0-100% range) /// - Useful for comparing different scales /// - Cannot handle zero actual values /// - Asymmetric (treats over/under predictions differently) /// /// Formula: /// MAPE = (1/n) * Σ|((actual - predicted) / actual)| * 100% /// /// Sources: /// https://en.wikipedia.org/wiki/Mean_absolute_percentage_error /// https://www.statisticshowto.com/mean-absolute-percentage-error-mape/ /// /// Note: Also known as MAPD (Mean Absolute Percentage Deviation) in some contexts /// [SkipLocalsInit] public sealed class Mape : AbstractBase { private readonly CircularBuffer _actualBuffer; private readonly CircularBuffer _predictedBuffer; /// The number of points over which to calculate the MAPE. /// Thrown when period is less than 1. [MethodImpl(MethodImplOptions.AggressiveInlining)] public Mape(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 = $"Mape(period={period})"; Init(); } /// The data source object that publishes updates. /// The number of points over which to calculate the MAPE. [MethodImpl(MethodImplOptions.AggressiveInlining)] public Mape(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 >= double.Epsilon ? Math.Abs((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 mape = 0; if (_actualBuffer.Count > 0) { ReadOnlySpan actualValues = _actualBuffer.GetSpan(); ReadOnlySpan predictedValues = _predictedBuffer.GetSpan(); double sumAbsolutePercentageError = 0; for (int i = 0; i < actualValues.Length; i++) { sumAbsolutePercentageError += CalculatePercentageError(actualValues[i], predictedValues[i]); } mape = sumAbsolutePercentageError / actualValues.Length; } IsHot = _index >= WarmupPeriod; return mape; } }