using System.Runtime.CompilerServices; namespace QuanTAlib; /// /// RAE: Relative Absolute Error /// A normalized error metric that compares the total absolute error to the total /// magnitude of actual values. RAE provides a scale-independent measure of error /// that is robust to the overall magnitude of the data. /// /// /// The RAE calculation process: /// 1. Calculates sum of absolute errors /// 2. Calculates sum of absolute actual values /// 3. Divides total error by total actual magnitude /// /// Key characteristics: /// - Scale-independent (normalized by actual values) /// - Range typically between 0 and 1 /// - Easy to interpret (0 is perfect, 1 means error equals data magnitude) /// - Robust to data scale changes /// - Less sensitive to outliers than squared errors /// /// Formula: /// RAE = Σ|actual - predicted| / Σ|actual| /// /// Sources: /// https://en.wikipedia.org/wiki/Relative_absolute_error /// https://www.sciencedirect.com/topics/engineering/relative-absolute-error /// /// Note: Values greater than 1 indicate predictions worse than using zero /// [SkipLocalsInit] public sealed class Rae : AbstractBase { private readonly CircularBuffer _actualBuffer; private readonly CircularBuffer _predictedBuffer; /// The number of points over which to calculate the RAE. /// Thrown when period is less than 1. [MethodImpl(MethodImplOptions.AggressiveInlining)] public Rae(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 = $"Rae(period={period})"; Init(); } /// The data source object that publishes updates. /// The number of points over which to calculate the RAE. [MethodImpl(MethodImplOptions.AggressiveInlining)] public Rae(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 error, double magnitude) CalculateErrorAndMagnitude(double actual, double predicted) { return (Math.Abs(actual - predicted), Math.Abs(actual)); } [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 rae = 0; if (_actualBuffer.Count > 0) { ReadOnlySpan actualValues = _actualBuffer.GetSpan(); ReadOnlySpan predictedValues = _predictedBuffer.GetSpan(); double sumAbsoluteError = 0; double sumAbsoluteActual = 0; for (int i = 0; i < actualValues.Length; i++) { var (error, magnitude) = CalculateErrorAndMagnitude(actualValues[i], predictedValues[i]); sumAbsoluteError += error; sumAbsoluteActual += magnitude; } rae = sumAbsoluteActual > 0 ? sumAbsoluteError / sumAbsoluteActual : 0; } IsHot = _index >= WarmupPeriod; return rae; } }