Timestampderived

Timestampderived refers to features created by transforming timestamp data into more informative attributes for analysis and modeling. It is used in data processing and machine learning to reveal temporal structure, seasonality, and cadence in events or observations. Typical timestampderived features include calendar components such as year, month, day, and day of week, as well as hour of day. To capture periodicity, hourly or daily values can be encoded cyclically using sine and cosine transforms. Other timestampderived attributes include indicators like is_weekend or is_holiday, and time-based aggregates such as counts, means, or sums over rolling windows. Additional features may include time since last event, time since first event, and variable spacing between timestamps.

Techniques for deriving timestampderived features include parsing date-time values, normalizing time zones, and handling daylight saving transitions. Analysts often apply resampling to align data to a common granularity, compute windowed statistics, and create lag features to preserve temporal order and avoid leakage. Timestampderived features are widely used in time-series forecasting, demand planning, anomaly detection, and customer analytics.

Example: in a transactional dataset with a timestamp column, timestampderived features might include year, month, day, hour, day_of_week, is_weekend, and time since last purchase. These features enable models to learn patterns tied to time, such as weekend spikes or monthly cycles.

See also: feature engineering, time-series analysis, datetime handling.