In this post, we will learn how to use DeepAR to forecast multiple time series using GluonTS in Python. DeepAR is a deep learning algorithm based on recurrent neural networks designed specifically for time series forecasting. It works by learning a model based on all the time series data, instead of creating a separate model for each one. In my experience, this often works better than creating a separate model for each time series....
You’ve probably heard about LSTMs, and might be curious about how they can help you with multiple time series forecasting. As machine learning practitioners, we come across various forecasting tasks, and choosing the right model can sometimes be a challenge. LSTMs have gained attention for their ability to handle long-term dependencies in sequential data, making them a promising choice for time series problems. By the end of this tutorial, you’ll have a deeper understanding of LSTMs and be better prepared to use them effectively for multiple time series forecasting projects....