feat(stats): add Savitzky-Golay smoothing filter

[Shizoqua]

Adds stats.SavitzkyGolay, a rolling Savitzky-Golay smoothing filter as requested in #1424.

Savitzky-Golay fits a polynomial of a given degree to a sliding window of observations by least squares and returns the smoothed value of the most recent point via the precomputed scipy.signal.savgol_coeffs dot product. Because the polynomial is evaluated at the last point of the window, the filter is causal and compatible with online learning.

>>> from river import stats
>>> stat = stats.SavitzkyGolay(window_size=5, polyorder=2)

• Coefficients computed once in __init__, applied over a fixed-size deque
• get() returns None until the window has enough observations
• window_size/polyorder follow scipy's constraint (polyorder < window_size, enforced by scipy)
• Registered in stats/__init__.py and the docs nav
• Release-note entry and CodSpeed benchmark included

Verification:

• pytest tests/stats — 175 passed (incl. doctests)
• Matches a manual least-squares polynomial fit to the same window; reproduces a degree-≤2 polynomial exactly
• ruff check / ruff format --check clean

DCO sign-off included.

[codspeed-hq]

Merging this PR will not alter performance

✅ 98 untouched benchmarks
🆕 1 new benchmark
⏩ 16 skipped benchmarks¹

Performance Changes

	Mode	Benchmark	BASE	HEAD	Efficiency
🆕	Simulation	test_savitzky_golay_update	N/A	5.1 ms	N/A

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_{Comparing Shizoqua:feat/1424-savitzky-golay (8c5cc9c) with main (0b8596f)}

Footnotes

1. 16 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩