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lib/forecasts/afirma/Afirma.md
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# AFIRMA: Autoregressive FIR Moving Average
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Forecast |
| **Inputs** | Source (close) |
| **Parameters** | `period`, `window` (default WindowType.BlackmanHarris), `leastSquares` (default false) |
| **Outputs** | Single series (Afirma) |
| **Output range** | Tracks input |
| **Warmup** | `period` bars |
### TL;DR
- AFIRMA is a Windowed Weighted Moving Average that replaces standard linear weighting with weights derived from signal processing window functions (...
- Parameterized by `period`, `window` (default windowtype.blackmanharris), `leastsquares` (default false).
- Output range: Tracks input.
- Requires `period` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "Standard Moving Averages assume linear or exponential weights. AFIRMA asks: what if we used signal processing window functions instead?"
AFIRMA is a Windowed Weighted Moving Average that replaces standard linear weighting with weights derived from signal processing window functions (Hanning, Hamming, Blackman, Blackman-Harris). This approach achieves specific frequency response characteristics tailored to noise reduction.
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## References
- Harris, F. J. (1978). "On the use of windows for harmonic analysis with the discrete Fourier transform." *Proceedings of the IEEE*, 66(1), 51-83.
- Nuttall, A. H. (1981). "Some windows with very good sidelobe behavior." *IEEE Transactions on Acoustics, Speech, and Signal Processing*, 29(1), 84-91.
- Nuttall, A. H. (1981). "Some windows with very good sidelobe behavior." *IEEE Transactions on Acoustics, Speech, and Signal Processing*, 29(1), 84-91.