Pytorch Transfomer
My implementation of the transformer architecture from the Attention is All you need paper applied to time series.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Dockerjenkins_tutorial | 693 | 5 years ago | 6 | apache-2.0 | Shell | |||||
| A repository for items learned in my Getting Started with Jenkins and Docker tutorial series | ||||||||||
| Transformer | 691 | a year ago | 6 | gpl-3.0 | Jupyter Notebook | |||||
| Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series. | ||||||||||
| Time Series Autoencoder | 386 | a year ago | 5 | apache-2.0 | Python | |||||
| :chart_with_upwards_trend: PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series :chart_with_upwards_trend: | ||||||||||
| Transformer_time_series | 331 | a year ago | 2 | Jupyter Notebook | ||||||
| Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting (NeurIPS 2019) | ||||||||||
| Deep Time Series Prediction | 287 | 2 years ago | 7 | Python | ||||||
| Seq2Seq, Bert, Transformer, WaveNet for time series prediction. | ||||||||||
| Time Series Papers | 262 | 4 years ago | 1 | |||||||
| 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 | ||||||||||
| Da Rnn | 242 | 3 years ago | 20 | Python | ||||||
| Dual-Stage Attention-Based Recurrent Neural Net for Time Series Prediction | ||||||||||
| Da Rnn | 234 | 3 years ago | Jupyter Notebook | |||||||
| 📃 **Unofficial** PyTorch Implementation of DA-RNN (arXiv:1704.02971) | ||||||||||
| Dsanet | 140 | 3 years ago | 2 | Python | ||||||
| Code for the CIKM 2019 paper "DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting". | ||||||||||
| Pytorch Transfomer | 95 | 3 years ago | 2 | Jupyter Notebook | ||||||
| My implementation of the transformer architecture from the Attention is All you need paper applied to time series. | ||||||||||
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