Pierdimi

4 ms·

1.
Exact O(1) Recursive Realization of Polynomial Fir Filters(zenodo.org)
2 points by Pierdimi 3mo ago|0 comments
2.
by Pierdimi 4mo ago
The statement "There is no need to rescan the window, both the computational cost and the additional memory remain constant as k increases" is inaccurate. The correct statement is: "There is no need to rescan the window; the
3.
by Pierdimi 4mo ago
A runnable implementation used for the comparative analysis against the Savitzky-Golay filter on a sawtooth signal is provided in Appendix B of the deposited working paper "Recursive Moving Polynomial Regression: A Unified Constant‑Tim
4.
by Pierdimi 4mo ago
Yes. The moving linear regression reduces to a fixed‑cost O(1) recurrence, in a way analogous to a recursive moving average. The internal state requires only the two previous estimates y1_hat(n−1) and y1_hat(n−2), the current sample y(n), t
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Show HN: O(1) Recursive Moving Linear Regression
2 points by Pierdimi 4mo ago|5 comments
6.
by Pierdimi 6mo ago
Hi. The Zenodo link is public and accessible — over a hundred users have downloaded the file.If you still see a block, you can use the DOI version: https://doi.org/10.5281/zenodo.19038620
7.
Recursive Moving Polynomial Regression – O(1) Constant Complexity(zenodo.org)
2 points by Pierdimi 6mo ago|3 comments
8.
by Pierdimi 6mo ago
We present a recursive formulation of the moving polynomial regression (RPM), valid for any degree m, with O(1) constant complexity per sample updates. For each fixed degree m, the same expression provides full flexibility in the choice of