QuanTAlib/lib/forecasts/_index.md

Forecasts

"Prediction is very difficult, especially about the future."  Niels Bohr

Forecasting and predictive models. Unlike reactive indicators that smooth past data, forecasts attempt to project future values. Extrapolation is inherently uncertain. Use with appropriate skepticism and position sizing.

Indicator Status

Indicator	Full Name	Status	Description
AFIRMA	Adaptive FIR Moving Average		Windowed sinc coefficients. Optimal frequency response. Can extrapolate.
CFO	Chande Forecast Oscillator	=�	Percentage difference between price and linear regression forecast.
MLP	Multilayer Perceptron	=�	Neural network regressor. Nonlinear pattern learning.
TSF	Time Series Forecast	=�	Linear regression projected forward. Standard extrapolation.

Status Key:  Implemented | =� Planned

Selection Guide

Use Case	Recommended	Why
Smooth extrapolation	AFIRMA	FIR with extrapolation coefficients. Configurable lookahead.
Linear trend projection	TSF	Simple, interpretable. Works when trend is linear.
Forecast deviation	CFO	Shows when price diverges from linear forecast.
Nonlinear patterns	MLP	Neural network learns complex relationships. Requires training.

Forecasting Principles

Aspect	Reality	Implication
Extrapolation risk	Markets are non-stationary	Short horizons more reliable
Model uncertainty	All models are wrong	Use ensemble or confidence intervals
Regime changes	Past patterns may not repeat	Monitor forecast errors
Overfitting	Complex models fit noise	Prefer simple models when possible

Forecasting is not prediction. It is disciplined extrapolation of patterns that may or may not persist.