Feature engineering in time series analysis involves the process of selecting, modifying, or creating additional features (variables) from the raw time series data to enhance the predictive power of machine learning models. This step is crucial because raw time series data can be complex, containing patterns, trends, and seasonality that may not be directly usable for modeling. By creating new features, developers can provide the model with more meaningful information that captures the temporal dynamics of the data, increasing the chances of accurate predictions.
One common practice in feature engineering for time series is to create lag features, where you use previous observations as input for the model. For instance, if you are predicting stock prices based on past prices, you might create features such as the price from the previous day (lag_1), two days ago (lag_2), and so on. Additionally, calculating rolling statistics like the moving average or rolling standard deviation over a specified window can help identify trends and fluctuations that are not immediately obvious. Features capturing the time aspect, such as day of the week, month, or even holidays, can also provide insights into seasonal effects impacting the target variable.
Another important aspect of feature engineering in time series is to address external factors by integrating additional data. For example, when forecasting electricity consumption, including weather data like temperature or humidity can improve model accuracy since these factors can influence energy usage patterns. Moreover, encoding cyclical features (like hours in a day or days in a week) using sine and cosine transformations can help capture the cyclical nature of time more effectively. By thoughtfully crafting these features, developers can build models that not only understand the historical patterns of data but also generalize better to future predictions.