Shobha
The best way to predict the future is to invent it
How do you perform feature engineering in time-series data?
Feature engineering in time series involves transforming raw data into meaningful features that can improve the predictive performance of a model. For example, adding a feature for "previous week's sales" can help in predicting future sales. Some common techniques include:
1.Extracting time-related attributes: This involves adding features like hour of the day, day of the week, month, and seasonality to capture temporal patterns in the data.
2.Creating lag features: These are features derived from previous observations, such as previous day's sales, previous week's sales, or previous month's sales. Lag features help models understand how past values influence future outcomes.
3.Rolling windows: This technique involves calculating rolling statistics over a fixed window of time, such as a 7-day moving average or rolling sum. It smooths out short-term fluctuations and helps capture long-term trends.
1.Extracting time-related attributes: This involves adding features like hour of the day, day of the week, month, and seasonality to capture temporal patterns in the data.
2.Creating lag features: These are features derived from previous observations, such as previous day's sales, previous week's sales, or previous month's sales. Lag features help models understand how past values influence future outcomes.
3.Rolling windows: This technique involves calculating rolling statistics over a fixed window of time, such as a 7-day moving average or rolling sum. It smooths out short-term fluctuations and helps capture long-term trends.