Rolling time series forecasting is a type of time series forecasting that involves making predictions for each individual time step in a time series, one at a time. This is in contrast to traditional time series forecasting, which involves making predictions for multiple future time steps at once. Rolling time series forecasting is often used in situations where the forecasting model needs to be updated frequently, such as in online retail or stock market prediction. It allows the model to incorporate the most up-to-date data into the predictions, which can improve the accuracy of the forecast. To perform rolling time series forecasting, the model is trained on a window of historical data, and then used to make a prediction for the next time step. This process is repeated for each time step in the time series, with the model being updated with new data as it becomes available.
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