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| 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')) | |