メニュー
Use scipy periodogram to identify dominant seasonal frequencies in a time series before selecting Fourier feature orders or ARIMA seasonal parameters
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Memory-efficient Keras generator that streams sharded CSV files in chunks, renders strokes to images on-the-fly, and yields batches for training on datasets too large for memory
Stack 1D convolutions for local feature extraction before bidirectional LSTMs to classify variable-length stroke sequences into hundreds of doodle categories
Partition a large dataset into N balanced shards using integer key modulo arithmetic for reproducible, class-interleaved splits across CSV files
Normalize raw stroke coordinates to 0-255 range, resample at uniform arc-length spacing, then apply Ramer-Douglas-Peucker simplification
Render stroke sequences to grayscale images with temporal intensity encoding where earlier strokes are brighter and later strokes fade to encode drawing order
Score each face in a video as anomalous by computing L2 distance from the embedding centroid of all faces, then convert to probability via logistic function
| name | timeseries-periodogram-seasonality-detection |
| description | Use scipy periodogram to identify dominant seasonal frequencies in a time series before selecting Fourier feature orders or ARIMA seasonal parameters |
Before adding seasonal features, determine which frequencies actually exist in the data. The periodogram decomposes a time series into its frequency components, revealing peaks at dominant cycles (weekly, monthly, annual). This avoids overfitting to phantom seasonality and guides the choice of Fourier order or SARIMA seasonal period.
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import periodogram
fs = 365.25 # samples per year (daily data)
freqs, spectrum = periodogram(
series.dropna().values,
fs=fs,
detrend="linear",
window="boxcar",
scaling="spectrum",
)
plt.figure(figsize=(10, 4))
plt.step(freqs, spectrum)
plt.xscale("log")
plt.xlabel("Cycles per year")
plt.ylabel("Power")
plt.axvline(1, color="r", alpha=0.3, label="Annual")
plt.axvline(52, color="g", alpha=0.3, label="Weekly")
plt.legend()
plt.show()
# Find top 3 peaks
top_idx = np.argsort(spectrum)[-3:]
dominant_periods = 1.0 / freqs[top_idx] # in days
scipy.signal.periodogramfs=365.25 (cycles/year), hourly → fs=8766"linear" removes simple trends; for nonstationary data, difference first"boxcar" (no taper) for maximum frequency resolution; "hann" reduces spectral leakage