Multi-step ahead forecasting

Information

• Multi-step ahead forecasting refers to the task of predicting multiple future time steps in a time series.
• This is a more challenging task than single-step forecasting, which involves predicting only the next time step.
• Multi-step ahead forecasting can be performed using a variety of techniques, including statistical models and machine learning algorithms.
• Some common approaches to multi-step ahead forecasting include recursive forecasting, where the model is retrained at each time step using previous predictions as inputs, and direct forecasting, where the model predicts all future time steps at once.
• When evaluating multi-step ahead forecasting models, it is important to use appropriate metrics, such as mean squared error or mean absolute error, that account for errors across all predicted time steps.