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e317359 | 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 | import numpy as np
import re
from tsfeatures import tsfeatures, stl_features, entropy, hurst, lumpiness, stability
import pandas as pd
# Define periods for different frequency strings
PERIODS = {'H': 24, 'D': 7, # hourly, daily
'M': 12, 'Q': 4, # monthly, quarterly
'W': 4, 'A': 1, # weekly, annual
'T': 60, 'S': 60, # minute, second
'L': 1000, 'U': 1000, 'N': 1000} # millisecond, microsecond, nanosecond
def infer_period(freq):
"""
Infer the period of a time series based on its frequency string.
Parameters:
- freq: Frequency string (e.g., 'H', 'D', '2A-DEC').
Returns:
- The period as an integer.
Raises:
- ValueError: If the frequency is not recognized.
"""
if '-' in freq:
freq = freq.split('-')[0]
if freq in PERIODS:
return PERIODS[freq]
elif freq.isalnum():
pattern = r"(\d+)([a-zA-Z]+)"
match = re.match(pattern, freq)
repeat_count, freq_str = match.groups()
return max(PERIODS[freq_str]//int(repeat_count), 1)
else:
raise ValueError(f"Frequency {freq} not recognized")
def get_ts_features(timeseries: np.ndarray, freq) -> float:
"""
Extract time series features using the tsfeatures package.
Parameters:
- timeseries: A numpy array representing the time series data.
- freq: Frequency string of the time series.
Returns:
- A DataFrame containing selected features: trend, seasonal_strength, entropy, hurst, lumpiness, stability.
"""
# Create a DataFrame with a date range and the time series data
panel = pd.DataFrame({'ds': pd.date_range(
start='1900-01-01', periods=len(timeseries), freq=freq), 'y': timeseries})
panel['unique_id'] = 1
# Compute features using tsfeatures
features_df = tsfeatures(panel, features=[
stl_features, entropy, hurst, lumpiness, stability], freq=infer_period(freq))
# Ensure all required columns are present, filling missing ones with NaN
for column in ['trend', 'seasonal_strength', 'entropy', 'hurst', 'lumpiness', 'stability']:
if column not in features_df.columns:
features_df[column] = np.nan
return features_df[['trend', 'seasonal_strength', 'entropy', 'hurst', 'lumpiness', 'stability']]
if __name__ == "__main__":
# Test the infer_period function with various frequency strings
print(infer_period('30T'))
print(infer_period('2A-DEC'))
print(infer_period('H'))
print(infer_period('A'))
print(infer_period('2A'))
print(infer_period('A-DEC'))
print(infer_period('A-JAN'))
print(infer_period('5S'))
# Generate random time series data and test feature extraction
timeseries = np.random.randn(100)
print(get_ts_features(timeseries, '30T'))
# Generate a time series with all zeros and test feature extraction
timeseries = np.zeros(100)
print(get_ts_features(timeseries, 'D'))
# Generate a time series with a trend and test feature extraction
timeseries = np.arange(100)
print(get_ts_features(timeseries, '30T'))
# Generate a time series with seasonality and test feature extraction
timeseries = np.array(
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1])
print(get_ts_features(timeseries, 'M'))
# Generate a time series with a trend in the first half and random in the second half
timeseries = np.concatenate([np.arange(50), np.random.randn(50)])
print(get_ts_features(timeseries, 'H'))
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