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Tags:
time-series
time-series-decomposition
benchmark
component-recovery
symbolic-regression
icml-2026
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File size: 7,455 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 | import numpy as np
import pickle
from dataclasses import dataclass, field
from typing import Dict, Any, List, Optional, Sequence, Tuple
from ..core import DecompResult
from ..registry import MethodRegistry
try:
import faiss
_HAS_FAISS = True
except ImportError:
_HAS_FAISS = False
@dataclass
class GaborClusterConfig:
fs: float = 1.0
win_len: int = 256
hop: int = 64
n_fft: Optional[int] = None
window_type: str = "gaussian"
gaussian_sigma: Optional[float] = None
n_clusters: int = 8
max_atoms: int = 200_000
use_log_amp: bool = True
random_state: int = 42
n_iter: int = 20
verbose: bool = False
@dataclass
class GaborClusterModel:
centroids: np.ndarray
mu: np.ndarray
sigma: np.ndarray
cfg: GaborClusterConfig
def save(self, path: str) -> None:
np.savez_compressed(
path,
centroids=self.centroids.astype(np.float32),
mu=self.mu.astype(np.float32),
sigma=self.sigma.astype(np.float32),
cfg=np.array([self.cfg], dtype=object),
)
@staticmethod
def load(path: str) -> "GaborClusterModel":
data = np.load(path, allow_pickle=True)
centroids = data["centroids"]
mu = data["mu"]
sigma = data["sigma"]
cfg = data["cfg"][0]
return GaborClusterModel(centroids=centroids, mu=mu, sigma=sigma, cfg=cfg)
def _make_window(L: int, wtype: str, sigma: Optional[float]) -> np.ndarray:
if wtype == "gaussian":
if sigma is None:
sigma = L / 6.0
n = np.arange(L) - (L - 1) / 2.0
w = np.exp(-0.5 * (n / sigma) ** 2)
return w / np.sqrt((w ** 2).sum())
elif wtype == "hann":
w = np.hanning(L)
return w / np.sqrt((w ** 2).sum())
else:
raise ValueError(f"Unsupported window_type={wtype}")
def _stft_rfft(x: np.ndarray, L: int, hop: int, n_fft: Optional[int], window: np.ndarray) -> np.ndarray:
x = np.asarray(x, dtype=float).ravel()
N = len(x)
if n_fft is None:
n_fft = 1 << int(np.ceil(np.log2(L)))
if N < L:
n_frames = 1
else:
n_frames = 1 + (N - L) // hop
Z = np.empty((n_frames, n_fft // 2 + 1), dtype=np.complex64)
for m in range(n_frames):
start = m * hop
seg = np.zeros(L, dtype=float)
if start + L <= N:
seg[:] = x[start:start + L]
else:
tail = N - start
if tail > 0:
seg[:tail] = x[start:]
segw = seg * window
Z[m, :] = np.fft.rfft(segw, n=n_fft)
return Z
def _istft_rfft(Z: np.ndarray, L: int, hop: int, n_fft: int, window: np.ndarray, length: int) -> np.ndarray:
M, K_r = Z.shape
x_rec = np.zeros(length + L, dtype=float)
win_acc = np.zeros(length + L, dtype=float)
for m in range(M):
frame = np.fft.irfft(Z[m, :], n=n_fft).real[:L]
start = m * hop
x_rec[start:start + L] += frame * window
win_acc[start:start + L] += window ** 2
nz = win_acc > 1e-12
x_out = np.zeros_like(x_rec)
x_out[nz] = x_rec[nz] / win_acc[nz]
return x_out[:length]
def _extract_gabor_features(x: np.ndarray, cfg: GaborClusterConfig, window: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
x = np.asarray(x, dtype=float).ravel()
L = cfg.win_len
hop = cfg.hop
n_fft = cfg.n_fft or (1 << int(np.ceil(np.log2(L))))
Z = _stft_rfft(x, L, hop, n_fft, window)
M, K_r = Z.shape
amp = np.abs(Z)
if cfg.use_log_amp:
amp_feat = np.log1p(amp)
else:
amp_feat = amp
if M > 1:
t_idx = np.linspace(0.0, 1.0, M)
else:
t_idx = np.array([0.0])
if K_r > 1:
f_idx = np.linspace(0.0, 1.0, K_r)
else:
f_idx = np.array([0.0])
T, F = np.meshgrid(t_idx, f_idx, indexing="ij")
feats = np.stack([T.ravel().astype(np.float32), F.ravel().astype(np.float32), amp_feat.ravel().astype(np.float32)], axis=1)
return feats, Z
def _assign_clusters_faiss(feats: np.ndarray, model: GaborClusterModel) -> np.ndarray:
if not _HAS_FAISS:
raise ImportError("faiss is required for GABOR_CLUSTER.")
X = (feats - model.mu) / model.sigma
X = X.astype(np.float32)
d = X.shape[1]
index = faiss.IndexFlatL2(d)
index.add(model.centroids.astype(np.float32))
D, I = index.search(X, 1)
return I.ravel()
@MethodRegistry.register("GABOR_CLUSTER")
def gabor_cluster_decompose(y: np.ndarray, params: Dict[str, Any]) -> DecompResult:
if not _HAS_FAISS:
raise ImportError("faiss is required for GABOR_CLUSTER.")
cfg_dict = params.copy()
model_path = cfg_dict.get("model_path")
model = cfg_dict.get("model")
max_clusters = cfg_dict.get("max_clusters")
if model is None:
if model_path:
model = GaborClusterModel.load(model_path)
else:
raise ValueError("GABOR_CLUSTER requires 'model_path' or 'model' in params.")
cfg = model.cfg
x = np.asarray(y, dtype=float).ravel()
N = len(x)
L = cfg.win_len
hop = cfg.hop
n_fft = cfg.n_fft or (1 << int(np.ceil(np.log2(L))))
window = _make_window(L, cfg.window_type, cfg.gaussian_sigma)
feats, Z = _extract_gabor_features(x, cfg, window)
labels = _assign_clusters_faiss(feats, model)
M, K_r = Z.shape
K = model.centroids.shape[0]
labels_2d = labels.reshape(M, K_r)
amp = np.abs(Z)
energy_per_cluster = np.zeros(K, dtype=float)
for j in range(K):
mask = (labels_2d == j)
if np.any(mask):
energy_per_cluster[j] = (amp[mask] ** 2).sum()
if max_clusters is not None and max_clusters < K:
keep_idx = np.argsort(energy_per_cluster)[-max_clusters:]
keep_mask = np.zeros(K, dtype=bool)
keep_mask[keep_idx] = True
else:
keep_mask = np.ones(K, dtype=bool)
components: Dict[str, np.ndarray] = {}
used_clusters = []
for j in range(K):
if not keep_mask[j]:
continue
mask = (labels_2d == j).astype(np.float32)
if not np.any(mask):
continue
Zj = Z * mask
xj = _istft_rfft(Zj, L, hop, n_fft, window, N)
components[f"Cluster_{j}"] = xj
used_clusters.append(j)
if components:
sum_comp = np.zeros_like(x)
for v in components.values():
sum_comp += v
residual = x - sum_comp
else:
residual = x.copy()
# Map to trend/season if possible (heuristic)
trend_freq_thr = float(cfg_dict.get("trend_freq_thr", 0.08))
trend = np.zeros_like(x)
season = np.zeros_like(x)
for key, val in components.items():
cluster_idx = int(key.split("_")[1])
# Centroid freq is at index 1 (normalized freq)
f_norm = float(model.centroids[cluster_idx, 1])
if f_norm <= trend_freq_thr:
trend += val
else:
season += val
return DecompResult(
trend=trend,
season=season,
residual=residual,
components=components,
meta={
"method": "GABOR_CLUSTER",
"n_clusters": K,
"used_clusters": used_clusters,
"max_clusters": max_clusters
}
)
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