Datasets:
Formats:
json
Languages:
English
Size:
< 1K
Tags:
time-series
time-series-decomposition
benchmark
component-recovery
symbolic-regression
icml-2026
License:
File size: 3,830 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 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | import numpy as np
from typing import Dict, Any, List, Optional
from ..core import DecompResult
from ..registry import MethodRegistry
try:
from PyEMD import EMD
_HAS_PYEMD = True
except ImportError:
_HAS_PYEMD = False
from .utils import dominant_frequency, aggregate_modes
def _dominant_frequency(x: np.ndarray, fs: float = 1.0) -> float:
return dominant_frequency(x, fs)
def _aggregate_modes(modes: np.ndarray, indices: Optional[List[int]]) -> np.ndarray:
return aggregate_modes(modes, indices)
@MethodRegistry.register("EMD")
def emd_decompose(
y: np.ndarray,
params: Dict[str, Any],
) -> DecompResult:
"""
Empirical Mode Decomposition wrapper using PyEMD with frequency-based grouping.
"""
if not _HAS_PYEMD:
raise ImportError("PyEMD is required for EMD decomposition. Install 'EMD-signal' or 'PyEMD'.")
y_arr = np.asarray(y, dtype=float)
T = y_arr.shape[0]
cfg = params.copy()
fs = float(cfg.get("fs", 1.0))
primary_period = cfg.get("primary_period")
primary_period = float(primary_period) if primary_period not in (None, 0) else None
n_imfs = cfg.get("n_imfs")
emd = EMD()
if n_imfs is not None:
imfs = emd.emd(y_arr, max_imf=int(n_imfs))
else:
imfs = emd.emd(y_arr)
imfs = np.asarray(imfs, dtype=float)
if imfs.ndim == 1:
imfs = imfs[np.newaxis, :]
num_imfs = imfs.shape[0]
if num_imfs == 0:
zeros = np.zeros_like(y_arr)
return DecompResult(
trend=zeros,
season=zeros,
residual=y_arr.copy(),
meta={"method": "EMD", "imfs": [], "dominant_frequencies": []}
)
dom_freqs = [_dominant_frequency(comp, fs=fs) for comp in imfs]
trend_imfs = list(cfg.get("trend_imfs", []))
season_imfs = list(cfg.get("season_imfs", []))
extra_freq: Dict[str, Any] = {}
if not trend_imfs and not season_imfs:
if primary_period is not None and primary_period > 0:
f0 = 1.0 / primary_period
tol = float(cfg.get("season_freq_tol_ratio", 0.25)) * f0
low_thresh = float(cfg.get("trend_freq_threshold", f0 / 4.0 if f0 else 0.05))
for idx, f_dom in enumerate(dom_freqs):
if f_dom <= max(low_thresh, 1e-8):
trend_imfs.append(idx)
elif f0 > 0 and abs(f_dom - f0) <= max(tol, 1e-8):
season_imfs.append(idx)
if not trend_imfs:
trend_imfs.append(num_imfs - 1)
if not season_imfs:
best_idx = int(np.argmin([abs(f - f0) for f in dom_freqs]))
season_imfs.append(best_idx)
extra_freq = {
"primary_period_used": primary_period,
"fs": fs,
"trend_freq_threshold": low_thresh,
"season_freq_tolerance": tol,
}
else:
if num_imfs >= 1:
trend_imfs.append(num_imfs - 1)
if num_imfs >= 2:
trend_imfs.append(num_imfs - 2)
if num_imfs >= 1:
season_imfs.append(0)
if num_imfs >= 3:
season_imfs.append(1)
trend = _aggregate_modes(imfs, trend_imfs)
season = _aggregate_modes(imfs, season_imfs)
residual = y_arr - trend - season
meta = {
"method": "EMD",
"imfs_shape": imfs.shape,
"dominant_frequencies": dom_freqs,
"trend_components": trend_imfs,
"season_components": season_imfs,
}
if extra_freq:
meta.update(extra_freq)
return DecompResult(
trend=trend,
season=season,
residual=residual,
meta=meta,
)
|