Advanced Time-Series Data Windowing Strategy

Create a PostgreSQL query that implements a sophisticated rolling window analysis with variable-length time intervals, supporting both fixed and dynamic window calculations. The solution must handle irregular time series data, account for missing timestamps, and provide configurable aggregation methods including weighted moving averages, exponential smoothing, and gap interpolation. Demonstrate how the query can efficiently process large datasets with minimal performance overhead.