Stars
The machine learning toolkit for time series analysis in Python
MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.
Algorithms for explaining machine learning models
A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing
ruptures: change point detection in Python
Deep Learning for Time Series Classification
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Generate Diverse Counterfactual Explanations for any machine learning model.
A lightweight, local-first, and 🆓 experiment tracking library from Hugging Face 🤗
An intuitive library to extract features from time series.
🤖🕰️ An MCP server that gives language models temporal awareness and time calculation abilities. Teaching AI the significance of the passage of time through collaborative tool development.
PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series
Shapley Interactions and Shapley Values for Machine Learning
Access and analyze historical weather and climate data with Python.
Diffusion Convolutional Recurrent Neural Network Implementation in PyTorch
PyPSA-Eur: A Sector-Coupled Open Optimisation Model of the European Energy System
Time Series Prediction with tf.contrib.timeseries
A library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard.
A Python module for use with Elsevier's APIs: Scopus, ScienceDirect, others.
simple and efficient python implemention of a series of adaptive filters. including time domain adaptive filters(lms、nlms、rls、ap、kalman)、nonlinear adaptive filters(volterra filter、functional link a…