ts-features-sculptor 1.0.1

A package for transforming time series features

ts_features_sculptor

A package for feature engineering on time series data.

Installation

pip install ts_features_sculptor

Example

A simple example of feature engineering creation:

import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from ts_features_sculptor import (
    ToDateTime,
    SortByTime,
    Tte,
    TimedRollingAggregator
)

data = {
    'time': [
        '2025-10-01 06:00:00',
        '2025-02-01 12:00:00',
        '2025-02-11 18:00:00',
        '2025-01-10 06:00:00'
    ],
    'value': [
        10., 11., 12., 11.
    ]
}
df = pd.DataFrame(data)

pippeline = Pipeline([
    ('to_datetime', ToDateTime(time_col="time")),
    ('sort_by_time', SortByTime(time_col="time")),
    ('tte', Tte(time_col="time")),
    ('time_rolling_aggregator', TimedRollingAggregator(
        time_col = "time",
        feature_col = "tte",
        window_days = 30,
        agg_funcs = ['mean', 'count'],
        fillna = np.nan
    ))
])

result_df = pippeline.transform(df)

print(result_df.to_string(index=False))
               time  value    tte  tte_time_rolling_mean_30  tte_time_rolling_count_30
2025-01-10 06:00:00   11.0  22.25                       NaN                        NaN
2025-02-01 12:00:00   11.0  10.25                     22.25                        1.0
2025-02-11 18:00:00   12.0 231.50                     10.25                        1.0
2025-10-01 06:00:00   10.0    NaN                       NaN                        NaN

Transformers

• ToDateTime - Converts string time values to datetime format
• SortByTime - Sorts data by timestamp.
• TimeValidator - Validates the correctness of timestamps.
• Tte - Computes time to event in days.
• Lag - Creates lag features (time-shifted values).
• RowRollingAggregator - Aggregates data using a fixed-row rolling window.
• TimedRollingAggregator -Aggregates data using a time-based rolling window (in days).
• DaysOfLife - Calculates the number of days since the start of observations (from the earliest date).
• DateTimeDecomposer - Decomposes timestamps into components (year, month, day, day of week, hour, etc.).
• Expanding - Computes expanding aggregates (cumulative statistics).
• Expression - Applies custom expressions to data using numpy functions.

Generators

• TemporalGenerator - Base abstract class for time series generators.
• FlexibleCyclicalGenerator - Generates weakly cyclical signals with high intra-cycle variability.
• StructuredCyclicalGenerator - Generates signals with strict cyclicity and low stochasticity.

License

MIT