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Tags:
time-series
time-series-decomposition
benchmark
component-recovery
symbolic-regression
icml-2026
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17b7ba4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | import numpy as np
from typing import List, Optional, Sequence
def dominant_frequency(x: np.ndarray, fs: float = 1.0) -> float:
x = np.asarray(x, dtype=float)
if len(x) < 2:
return 0.0
x = x - np.mean(x)
spectrum = np.abs(np.fft.rfft(x))
freqs = np.fft.rfftfreq(len(x), d=1.0 / fs if fs > 0 else 1.0)
if spectrum.size <= 1:
return 0.0
idx = int(np.argmax(spectrum[1:]) + 1) if spectrum.size > 1 else 0
return float(freqs[idx]) if idx < len(freqs) else 0.0
def ensure_period_list(
periods: Optional[Sequence[float]],
fallback: Optional[float],
series_length: int,
) -> List[int]:
cleaned: List[int] = []
if periods:
for val in periods:
try:
p_int = int(round(float(val)))
except (TypeError, ValueError):
continue
if p_int >= 2:
cleaned.append(p_int)
if not cleaned and fallback:
try:
p = int(round(float(fallback)))
if p >= 2:
cleaned.append(p)
except (TypeError, ValueError):
pass
if not cleaned:
approx = max(2, series_length // 10)
cleaned.append(approx)
# deduplicate while preserving order
seen = set()
unique: List[int] = []
for p in cleaned:
if p not in seen:
seen.add(p)
unique.append(p)
return unique
def aggregate_modes(modes: np.ndarray, indices: Optional[List[int]]) -> np.ndarray:
if indices is None or len(indices) == 0:
return np.zeros(modes.shape[1], dtype=float)
valid = [idx for idx in indices if 0 <= idx < modes.shape[0]]
if not valid:
return np.zeros(modes.shape[1], dtype=float)
return np.sum(modes[valid, :], axis=0)
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