Engineer Time-Based Features for ML Models

Time-Based Feature Engineering transforms raw datetime data into features representing seasonality, trends, cycles, and temporal relationships. Its Python transformations include calendar extraction, cyclical encoding, historical lags, rolling statistics, event recency, and period comparisons. These are building blocks for evaluating temporal signals in a machine learning model.

Calendar features include year, month, day, hour, minute, weekday, day of year, ISO week, and quarter. Boolean indicators identify weekends and the start or end of months, quarters, and years. Cyclical features use sine and cosine pairs so adjacent positions in a recurring calendar cycle can remain related across a boundary. Encoding supports hourly, weekly, monthly, and annual cycles, using 365.25 in the annual encoding.

Lag examples shift a target column by selected offsets, with defaults of 1, 7, and 30. Rolling examples use windows of 7, 30, and 90 to calculate means, standard deviations, minima, and maxima. Both sections distinguish a simple series from entity-aware processing, such as per-customer or per-product history.

Event-recency features locate the last occurrence of an indicated event, calculate elapsed days, and create flags for recent occurrences. Temporal aggregation examples compare weekly and monthly means, calculate changes between periods for grouped entities, and merge comparisons back into the data.

Use the skill when timestamped observations need features that capture seasonal behavior or historical relationships. The material calls for time-based cross-validation to address leakage, stability testing across periods, monitoring changing feature importance, and removing highly correlated temporal features. It does not make improvement automatic: predictive power must be evaluated across different time periods.

Domain checks include business hours versus after-hours activity, holidays and special events, seasonal adjustment, and time zones for global datasets. These considerations determine whether a calendar or historical signal is meaningful in the intended setting.

Inputs are a dataframe and datetime column, plus an optional target column, entity column, or event indicators. Outputs are derived feature dataframes and an expanded dataset assembled from the selected transformations. The combined pipeline appends calendar and cyclical features, then includes lag and rolling features when a target is provided. Its combination step is:

    final_df = pd.concat(features, axis=1)
    
    return final_df

The examples use pandas datetime accessors, group operations, shifts, rolling windows, and concatenation with numpy trigonometric functions. Performance guidance recommends appropriate data types such as int8 for boolean features, numba for efficient rolling work on large datasets, caching expensive computations, and categorical encoding for high-cardinality time features.