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.
Multi-output time series regression can forecast several steps of a time series at once. The number of neurons in the final output layer determines how many steps the model can predict. Models with one output return single-step forecasts. Models with various outputs can return entire series of time steps and thus deliver a more detailed … Read more
Many time forecasting problems can be solved by predicting just one step into the future. However, some problems require a forecast for an extended period of time, which calls for a multi-step time series forecasting approach. This approach involves modeling the distribution of future values of a signal over a prediction horizon. In this article, … Read more
