# 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](lib/forecasts/afirma/Afirma.md) | Adaptive FIR Moving Average |  | Windowed sinc coefficients. Optimal frequency response. Can extrapolate. |
| CFO | Chande Forecast Oscillator | =Ë | Percentage difference between price and linear regression forecast. |
| MLP | Multilayer Perceptron | =Ë | Neural network regressor. Nonlinear pattern learning. |
| TSF | Time Series Forecast | =Ë | Linear regression projected forward. Standard extrapolation. |

**Status Key:**  Implemented | =Ë 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.