mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-19 19:18:05 +00:00
validation and profiles
This commit is contained in:
@@ -118,6 +118,21 @@ $$
|
||||
|
||||
## Performance Profile
|
||||
|
||||
### Operation Count (Streaming Mode)
|
||||
|
||||
AFIRMA chains AR model estimation with IIR/FIR filtering — O(p) per bar where p = AR order.
|
||||
|
||||
| Operation | Count | Cost (cycles) | Subtotal |
|
||||
| :--- | :---: | :---: | :---: |
|
||||
| AR coefficient estimation (p terms) | p | 4 cy | ~4p cy |
|
||||
| FIR forward pass (p multiplies) | p | 1 cy | ~p cy |
|
||||
| IIR feedback pass (p multiplies) | p | 1 cy | ~p cy |
|
||||
| Output computation via FMA | 1 | 1 cy | ~1 cy |
|
||||
| NaN guard + state update | 1 | 2 cy | ~2 cy |
|
||||
| **Total (p=8)** | **O(p)** | — | **~49 cy** |
|
||||
|
||||
O(p) per bar where p = AR order. AR coefficient estimation dominates; FMA-fused filter passes are cheap. Streaming mode maintains p state variables.
|
||||
|
||||
| Metric | Value | Notes |
|
||||
| :--- | :---: | :--- |
|
||||
| **Complexity** | O(P) | Convolution per bar |
|
||||
|
||||
Reference in New Issue
Block a user