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Formats:
json
Languages:
English
Size:
< 1K
Tags:
time-series
time-series-decomposition
benchmark
component-recovery
symbolic-regression
icml-2026
License:
File size: 2,385 Bytes
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 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | import numpy as np
from typing import Dict, Any, List, Optional
from ..core import DecompResult
from ..registry import MethodRegistry
try:
import pywt
_HAS_PYWT = True
except ImportError:
_HAS_PYWT = False
def _reconstruct_from_levels(
coeffs: List[np.ndarray],
keep_levels: List[int],
wavelet: str,
target_len: int,
) -> np.ndarray:
rec_coeffs: List[Optional[np.ndarray]] = []
for idx, coeff in enumerate(coeffs):
if idx in (keep_levels or []):
rec_coeffs.append(np.copy(coeff))
else:
rec_coeffs.append(np.zeros_like(coeff))
recon = pywt.waverec(rec_coeffs, wavelet)
if recon.shape[0] > target_len:
recon = recon[:target_len]
elif recon.shape[0] < target_len:
pad = target_len - recon.shape[0]
recon = np.pad(recon, (0, pad), mode="edge")
return recon
@MethodRegistry.register("WAVELET")
def wavelet_decompose(
y: np.ndarray,
params: Dict[str, Any],
) -> DecompResult:
"""
Wavelet-based multi-scale decomposition using PyWavelets.
"""
if not _HAS_PYWT:
raise ImportError("PyWavelets (pywt) is required for wavelet decomposition. Install 'pywt'.")
cfg = params.copy()
wavelet_name = cfg.get("wavelet", "db4")
level = cfg.get("level")
wavelet = pywt.Wavelet(wavelet_name)
max_level = pywt.dwt_max_level(len(y), wavelet.dec_len)
if level is None:
level = max(1, min(5, max_level))
coeffs = pywt.wavedec(y, wavelet, level=level)
num_coeffs = len(coeffs)
trend_levels = cfg.get("trend_levels")
season_levels = cfg.get("season_levels")
if trend_levels is None:
trend_levels = [0]
if season_levels is None and num_coeffs > 2:
season_levels = [1, 2]
elif season_levels is None:
season_levels = [idx for idx in range(1, num_coeffs)]
trend = _reconstruct_from_levels(coeffs, trend_levels, wavelet_name, len(y))
season = _reconstruct_from_levels(coeffs, season_levels, wavelet_name, len(y))
residual = y - trend - season
return DecompResult(
trend=trend,
season=season,
residual=residual,
meta={
"method": "WAVELET",
"params": cfg,
"coeffs_shapes": [c.shape for c in coeffs]
},
)
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