awesome_time_series_in_python

Why we included this project

Time series work in Python spreads across a lot of small, single-purpose packages, and this list is a handy map of that territory. It groups libraries by what they do, so forecasting tools like Darts and ETNA sit alongside feature extraction utilities, shapelet discovery, and financial-economics helpers. That makes it a quick way to see what exists before you commit to reading a dozen READMEs. The maintainers also include notes on standardizing time series formats, which is the kind of detail you usually have to dig for yourself. It is a curated index rather than something you install, but for anyone trying to pick a library for a forecasting or signal-processing task, it saves real time.