deep-learning-time-series

Why we included this project

For anyone getting into deep learning for time series forecasting, this repository is a handy starting point. It collects research papers, competition write-ups, conference talks, and linked code so you can move from reading about a method to trying it. The comparison between classical statistical approaches and newer architectures like transformers, LSTMs, and graph networks is especially useful for deciding what to test first. It also points to datasets and code resources, making it more than a plain bibliography. If you're scoping a forecasting project, this is a quick way to see which techniques have mature implementations worth experimenting with.