Multi-step time series forecasting is the process of predicting multiple future time steps of a time series based on its historical data. This is in contrast to one-step time series forecasting, which involves predicting only the next time step of a time series. Multi-step time series forecasting is a challenging problem because it requires the model to not only capture the underlying trends and patterns in the data, but also to make accurate long-term predictions. There are many different techniques that can be used for this purpose, including machine learning algorithms like recurrent neural networks and random forests.
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