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a-classifier-with-pytorch

anomaly-detection-resources

Anomaly detection related books, papers, videos, and toolboxes

attention_based_multihead_model_for_aircraft_engine_rul_prediction_

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

autots

Automated Time Series Forecasting

awesome-deep-learning-based-time-series-forecasting

cit_lstm_timeseries

LSTM Model for Electric Load Forecasting

convrnn_for_rul_estimation

Code used in Thesis "Convolutional Recurrent Neural Networks for Remaining Useful Life Prediction in Mechanical Systems".

da-rnn

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

deep-learning-for-time-series-forecasting

This repository is designed to teach you, step-by-step, how to develop deep learning methods for time series forecasting with concrete and executable examples in Python.

deep-learning-time-series

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

deepar-pytorch

DeepAR implementation in PyTorch for regression

deepar-pytorch-1

deepar-torch

This is a pytorch version of DeepAR.

demand_forecast

estimation-of-remaining-useful-life-using-cnn

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

gluon-ts

Probabilistic time series modeling in Python

kaggle-web-traffic

1st place solution

lstm-pytorch-remaining-useful-life-prediction

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 prognostics and health management (ICPHM) (pp. 88-95). IEEE.

lstm_fusion

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

multidimensional-time-series-with-transformer

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

pytorch-cnn-for-rul-prediction

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 estimation of remaining useful life. In International conference on database systems for advanced applications (pp. 214-228). Springer, Cham.

pytorch-forecasting

Time series forecasting with PyTorch

pytorch-lstm-for-rul-prediction

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., & Gupta, C. (2017, June). Long short-term memory network for remaining useful life estimation.

pytorch-transformer-for-rul-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 life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing, 1-10.

remaining-useful-life-estimation-of-rocket-engines

Similarity matching based remaining useful life estimation

remaining-useful-life-estimation-variational

rul-net

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

rul-prediction

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

time-series-arima-xgboost-rnn

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

time-series-forecasting-keras

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