Time series forecasting models for weather features
This report is an introduction to time series forecasting using Deep learning. It builds a few different styles of models including Convolutional and Recurrent Neural Networks (CNNs and RNNs). Unlike other machine learning algorithms, long short-term memory recurrent neural networks are capable of automatically learning features from sequence data, support multi-variate data, and can output a variable length sequences that can be used for multi-step forecasting. In this tutorial, you will discover how to develop long short-term memory recurrent neural networks for multi-step time series forecasting of weather features.
This notebook can be viewed on nbviewer:
