QuanTAlib/lib/errors/_index.md

Errors

Error metrics and performance indicators for model/strategy evaluation. All error indicators accept two input series (actual and predicted values) and compute rolling error metrics over a configurable period.

Two-Input Pattern

All error indicators in this category follow a consistent dual-input API:

// Streaming mode
var mae = new Mae(period: 14);
var result = mae.Update(actualValue, predictedValue);

// Batch mode
var maeSeries = Mae.Calculate(actualSeries, predictedSeries, period: 14);

// Span mode (zero-allocation)
Mae.Batch(actualSpan, predictedSpan, outputSpan, period: 14);

Indicator Reference

Indicator	Full Name	Description
HUBER	Huber Loss	Combines MSE and MAE; less sensitive to outliers
MAE	Mean Absolute Error	Average of absolute differences
MAPD	Mean Absolute Percentage Deviation	Percentage error relative to mean of actual and predicted
MAPE	Mean Absolute Percentage Error	Percentage error relative to actual values
MASE	Mean Absolute Scaled Error	Scale-free error using naive forecast as baseline
ME	Mean Error	Average of signed differences (bias detector)
MPE	Mean Percentage Error	Signed percentage error (directional bias)
MSE	Mean Squared Error	Average of squared differences
MSLE	Mean Squared Logarithmic Error	MSE on log-transformed values
RAE	Relative Absolute Error	Absolute error relative to mean predictor
RMSE	Root Mean Squared Error	Square root of MSE; same units as input
RMSLE	Root Mean Squared Logarithmic Error	RMSE on log-transformed values
RSE	Relative Squared Error	Squared error relative to mean predictor
RSQUARED	Coefficient of Determination	Proportion of variance explained (1 - RSE)
SMAPE	Symmetric Mean Absolute Percentage Error	Bounded percentage error (0-200%)

Choosing an Error Metric

By Use Case

Use Case	Recommended Metrics
General accuracy	MAE, RMSE
Outlier-robust	MAE, Huber, MASE
Percentage interpretation	MAPE, SMAPE, MAPD
Bias detection	ME, MPE
Scale-free comparison	MASE, RAE, RSE
Model quality score	R², RSE
Log-scale data	MSLE, RMSLE

By Properties

Metric	Scale	Outlier Sensitivity	Interpretability
MAE	Original units	Low	High
MSE	Squared units	High	Medium
RMSE	Original units	High	High
MAPE	Percentage	Medium	High
SMAPE	0-200%	Medium	High
Huber	Original units	Low (configurable)	Medium
R²	0-1 (for good models)	High	Very High