tslearn and Time-Series-Library
These tools are competitors, with tslearn providing a more established, general-purpose machine learning toolkit for time series analysis, while Time-Series-Library offers a collection of advanced deep learning models specifically for time series, indicating a choice between traditional ML and state-of-the-art deep learning approaches.
About tslearn
tslearn-team/tslearn
The machine learning toolkit for time series analysis in Python
This toolkit helps data scientists and machine learning engineers analyze sequential data by providing specialized algorithms for time series. You input raw time series data, and it helps you preprocess, classify, cluster, or predict trends within that data. It's designed for practitioners who work with data that changes over time, such as sensor readings, stock prices, or patient vitals.
About Time-Series-Library
thuml/Time-Series-Library
A Library for Advanced Deep Time Series Models for General Time Series Analysis.
This library helps deep learning researchers evaluate and develop advanced deep time series models. It takes raw time series data as input and provides outputs for tasks like long- and short-term forecasting, identifying anomalies, filling in missing data (imputation), and classifying time series patterns. It's designed for researchers specializing in deep learning, particularly those working with time series data.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work