The process begins by loading the TimeSeries Features dataset containing user transactions with attributes such as user ID, transaction date, amount, item, and category. The Time Series Features node computes temporal metrics like days since the last transaction, time until the next transaction, transaction frequency, and rolling averages.

It also derives contextual insights such as day of week, time of day, and seasonality to analyze patterns in user purchasing behavior. The enriched dataset, containing both original and derived time-based columns, is displayed using Print N Rows for review.

This workflow enhances data for modeling by capturing trends, seasonality, and time-driven user behavior efficiently through Spark’s scalable processing.