You are conducting feature engineering to prepuce data for further analysis. The data includes seasonal patterns on inventory requirements. You need to select the appropriate method to conduct feature

Exponential Smoothing (ETS) function is primarily a forecasting technique for time series, not a feature engineering method to extract or transform seasonal patterns for further analysis.

One Class Support Vector Machine module is used for anomaly detection in unlabeled data, focusing on identifying outliers rather than engineering features from seasonal patterns.

Time Series Anomaly Detection module is designed to identify unusual data points in time series, not to conduct general feature engineering on inherent seasonal patterns.

A Finite Impulse Response (FIR) Filter module is suitable for feature engineering on data with seasonal patterns because FIR filters are digital signal processing tools that can be designed to isolate, smooth, or extract specific frequency components and trends, effectively highlighting or modifying seasonal patterns in time-series data.