Popular GitHub Repositories for Multivariate Calculus

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.

Book_5_《统计至简》 | 鸢尾花书：从加减乘除到机器学习；上架！

A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation/classification/clustering/forecasting/anomaly detection/cleaning on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values

A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.

Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)

The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI

Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.

PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series

Multivariate LSTM Fully Convolutional Networks for Time Series Classification

This repository contains a reading list of papers on Time Series Segmentation. This repository is still being continuously improved.

The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516

Code for our SIGKDD'22 paper Pre-training-Enhanced Spatial-Temporal Graph Neural Network For Multivariate Time Series Forecasting.

Unsupervised Scalable Representation Learning for Multivariate Time Series: Experiments

Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.

Promotes development of ML algorithms for early detection and classification of undesirable events in offshore oil wells.

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).

Code & Data for "Tabular Transformers for Modeling Multivariate Time Series" (ICASSP, 2021)

Complete path for a beginner to become a Machine Learning Scientist!

A Full-Pipeline Automated Time Series (AutoTS) Analysis Toolkit.