What are Time Series Features?
Time series features are specific attributes derived from time-based data that enhance the performance of machine learning models. These features help capture the temporal dynamics and trends in data collected over time.
Types of Time Series Features
- Lag Features: These features represent the value of the target variable at previous time points. For example, if predicting temperature, the temperature from the previous day can be a significant predictor.
- Rolling Statistics: These include moving averages or rolling sums over a defined period, which help to smooth out fluctuations and identify trends.
- Date/Time Components: Breaking down the timestamp into components such as year, month, day, hour, and day of the week can capture seasonal patterns effectively.
- Seasonal Decomposition: This involves decomposing the time series into its seasonal, trend, and residual components, allowing the model to learn from these distinct parts.
Importance in Machine Learning
Incorporating these features improves model accuracy significantly, enabling better forecasting and anomaly detection. Well-engineered time series features leverage historical information, making them valuable in applications like financial forecasting, supply chain management, and weather prediction.
By effectively implementing time series features, data scientists can enhance the insights gained from temporal data.