File size: 2,256 Bytes
c4b87d2 |
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 |
import numpy as np
import torch
from scipy import signal
from statsmodels.tsa.stattools import acf
def lempel_ziv_complexity(binary_sequence: np.ndarray) -> int:
"""Computes the Lempel-Ziv complexity of a binary sequence."""
sub_strings = set()
n = len(binary_sequence)
i = 0
count = 0
while i < n:
sub_str = ""
for j in range(i, n):
sub_str += str(binary_sequence[j])
if sub_str not in sub_strings:
sub_strings.add(sub_str)
count += 1
i = j + 1
break
else:
i += 1
return count
def is_low_quality(
series: torch.Tensor,
autocorr_threshold: float = 0.2,
snr_threshold: float = 0.5,
complexity_threshold: float = 0.4,
) -> bool:
"""
Returns True if the series appears non-forecastable (noise-like):
- weak autocorrelation
- low SNR proxy
- high normalized Lempel-Ziv complexity
"""
x = series.squeeze().detach().cpu().numpy()
if x.size < 20:
return True
if np.var(x) < 1e-10:
return True
x_detrended = signal.detrend(x)
try:
max_lags = min(len(x_detrended) // 4, 40)
if max_lags < 1:
autocorr_strength = 0.0
else:
acf_vals = acf(x_detrended, nlags=max_lags, fft=True)[1:]
autocorr_strength = float(np.max(np.abs(acf_vals)))
except Exception:
autocorr_strength = 0.0
win_size = max(3, min(len(x) // 10, 15))
signal_est = np.convolve(x, np.ones(win_size) / win_size, mode="valid")
noise_est = x[win_size - 1 :] - signal_est
var_signal = float(np.var(signal_est))
var_noise = float(np.var(noise_est))
snr_proxy = var_signal / var_noise if var_noise > 1e-8 else 1.0
median_val = float(np.median(x_detrended))
binary_seq = (x_detrended > median_val).astype(np.uint8)
complexity_score = lempel_ziv_complexity(binary_seq)
normalized_complexity = complexity_score / max(1, len(binary_seq))
is_random_like = (snr_proxy < snr_threshold) and (
normalized_complexity > complexity_threshold
)
is_uncorrelated = autocorr_strength < autocorr_threshold
return bool(is_uncorrelated and is_random_like)
|