# tsai — State of the Art Machine Learning for Time Series, Part 1.

There are more cool time series libraries for Python than you can shake a stick at. You might have heard of some of them:

Each of these libraries has different methods for dealing with the various time series learning tasks — regression, classification and forecasting. Where they tend to differ is in the selection of methods they use, ranging from traditional statistical methods (e.g. ARIMA), to dynamic time series warping, symbolic time series approximations, and more.

For those who are interested in time series classification (TSC)— let’s talk about two types of machine learning methods that are popular these days. One is ROCKET, and the other is deep learning. For an overview into TSC methods and ROCKET in particular, you would do well to read the following posts by Alexandra Amidon:

- ROCKET : Fast and Accurate Time Series Classification
- A Brief Survey of Time Series Classification Algorithms

# ROCKET vs. Deep Learning

The tldr is this: ROCKET is one of the best off-the-shelf, general purpose of time series classification algorithms out there. And then there is Mini-ROCKET, which is faster to train without much loss (if any) in performance. Both are fairly fast. Anecdotally, I would say that…