Popular GitHub Repositories for Univariate Timeseries

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

Probabilistic time series modeling in Python

Time series forecasting with PyTorch

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

NeuralProphet: A simple forecasting package

The machine learning toolkit for time series analysis in Python

A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

Synthetic data generators for tabular and time-series data

A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals.

一款多语言的API，满足您所有可观测性需求。轻量、快速，可替代LGTM :rocket: 独立的OpenTelemetry、Loki、Prometheus、Tempo、Pyroscope兼容 :star:

Portfolio optimization with deep learning.

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

Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series.

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

tfts: Time Series Deep Learning Models in TensorFlow

Codebase for the paper LSTM Fully Convolutional Networks for Time Series Classification

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.

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

Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".