maria ungdom kristianstad. 2. n_step_out : Specify how much multi-step data we want to forecast. Multi-Step Forecast for Multivariate Time Series. Congratulations, you have learned how to implement multivariate multi-step time series forecasting using TF 2.0 / Keras. Time Series L'inscription et faire des offres sont gratuits. Multivariate Time Series Forecasting at time t+m with LSTMs in Keras The way we can do this, with Keras, is by wiring the LSTM hidden states to sets of consecutive outputs of the same lenght. Thus, if we want to produce predictions for 12 months, our LSTM should have a hidden state length of 12. These 12 time steps will then get wired to 12 linear predictor unites using keras::time_districuted () wrapper. Define and Fit Model. multivariate time series forecasting with lstms in keras Multi-Step Multivariate Time-Series Forecasting using LSTM