Probabilistic time series modeling in Python

Time series forecasting with PyTorch

Automated Time Series Forecasting

DeepAR implementation in PyTorch for regression

Anomaly detection related books, papers, videos, and toolboxes

List of papers, code and experiments using deep learning for time series forecasting

TensorFlow Probability；Time series model

Attention-based multihead model for optimized aircraft engine remaining useful life prediction

Transformer implementation with PyTorch for remaining useful life prediction on turbofan engine with NASA CMAPSS data set. Inspired by Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful l…

Remaining useful life estimation of NASA turbofan jet engines using data driven approaches which include regression models, LSTM neural networks and hybrid model which is combination of VAR with LSTM

An experiemtal review on deep learning architectures for time series forecasting

transformer/self-attention for Multidimensional time series forecasting 使用transformer架构实现多维时间预测

PyTorch implementation of CNN for remaining useful life prediction. Inspired by Babu, G. S., Zhao, P., & Li, X. L. (2016, April). Deep convolutional neural network-based regression approach for est…

PyTorch implementation of remaining useful life prediction with long-short term memories (LSTM), performing on NASA C-MAPSS data sets. Partially inspired by Zheng, S., Ristovski, K., Farahat, A., &…

Inspired by Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017, June). Long short-term memory network for remaining useful life estimation. In 2017 IEEE international conference on prognostic…

Acquiring data from Alpha Vantage and predicting stock prices with PyTorch's LSTM

📃 𝖀𝖓𝖔𝖋𝖋𝖎𝖈𝖎𝖆𝖑 PyTorch Implementation of DA-RNN (arXiv:1704.02971)

Deep learning approach for estimation of Remaining Useful Life (RUL) of an engine

This is a pytorch version of DeepAR.

data set and models for IEEE access paper "Remaining Useful Life Estimation Using Long Short-Term Memory Neural Networks and Deep Fusion"

List of awesome papers about time series, mainly including algorithms based on machine learning | 收录时间序列分析中各个研究领域的高水平文章，主要包含基于机器学习的算法

Time series forecasting for individual household power prediction: ARIMA, xgboost, RNN

Convolutional Neural Network based regression approach for estimating machinery's remaining useful life

time series forecasting using keras, inlcuding LSTM,RNN,MLP,GRU,SVR and multi-lag training and forecasting method, ICONIP2017 paper.