Alternatives and similar repositories for time-series-prediction-lstm-pytorch

Users that are interested in time-series-prediction-lstm-pytorch are comparing it to the libraries listed below

• zhangxu0307 / time_series_forecasting_pytorch
  time series forecasting using pytorch，including ANN,RNN,LSTM,GRU and TSR-RNN，experimental code
  ☆410Updated 6 years ago
• jsyoon0823 / Time-series-prediction
  Basic RNN, LSTM, GRU, and Attention for time-series prediction
  ☆176Updated 10 months ago
• nok-halfspace / Transformer-Time-Series-Forecasting
  ☆396Updated 4 years ago
• bighuang624 / DSANet
  Code for the CIKM 2019 paper "DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting".
  ☆260Updated last year
• CVxTz / time_series_forecasting
  ☆204Updated 3 years ago
• Zhenye-Na / DA-RNN
  📃 𝖀𝖓𝖔𝖋𝖋𝖎𝖈𝖎𝖆𝖑 PyTorch Implementation of DA-RNN (arXiv:1704.02971)
  ☆425Updated 4 years ago
• Vsooong / multivariate-prediction
  time-series-predictoin(LSTNet,SAB,Transformer...)
  ☆142Updated 6 years ago
• LiamMaclean216 / Pytorch-Transfomer
  My implementation of the transformer architecture from the Attention is All you need paper applied to time series.
  ☆321Updated 4 years ago
• ghsama / ConvTransformerTimeSeries
  Convolutional Transformer for time series
  ☆281Updated 5 years ago
• sarikayamehmet / cnn-bilstm-attention
  Multivariate Time Series Prediction using Keras (CNN BiLSTM Attention)
  ☆91Updated 4 years ago
• RuifMaxx / 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
  ☆235Updated 2 years ago
• dehoyosb / temporal_fusion_transformer_pytorch
  ☆227Updated 5 years ago
• lkulowski / LSTM_encoder_decoder
  Build a LSTM encoder-decoder using PyTorch to make sequence-to-sequence prediction for time series data
  ☆427Updated 4 years ago
• zhykoties / TimeSeries
  Implementation of deep learning models for time series in PyTorch.
  ☆390Updated 5 years ago
• Wizaron / deep-forecast-pytorch
  Wind Speed Prediction using LSTMs in PyTorch (https://arxiv.org/pdf/1707.08110.pdf)
  ☆188Updated 7 years ago
• JulesBelveze / time-series-autoencoder
  PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series
  ☆683Updated 5 months ago
• mlpotter / Transformer_Time_Series
  Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting (NeurIPS 2019)
  ☆591Updated 2 years ago
• shunyaoshih / TPA-LSTM
  Temporal Pattern Attention for Multivariate Time Series Forecasting
  ☆729Updated 6 years ago
• fbadine / LSTNet
  A Tensorflow / Keras implementation of "Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks" paper
  ☆176Updated 3 years ago
• zhangxu0307 / time-series-forecasting-keras
  time series forecasting using keras, inlcuding LSTM,RNN,MLP,GRU,SVR and multi-lag training and forecasting method, ICONIP2017 paper.
  ☆120Updated 6 years ago
• lpphd / multivariate-attention-tcn
  ☆97Updated 2 years ago
• mattsherar / Temporal_Fusion_Transform
  Pytorch Implementation of Google's TFT
  ☆271Updated 5 years ago
• stevinc / Transformer_Timeseries
  Pytorch code for Google's Temporal Fusion Transformer
  ☆99Updated 3 years ago
• husnejahan / DeepAR-pytorch
  ☆266Updated 4 years ago
• hihihihiwsf / AST
  Adversarial Sparse Transformer for Time Series Forecasting
  ☆140Updated 3 years ago
• ChiYaoLa / TimeSeriesPredict
  used for Stock Prodiction&power prediction&Traffic prediction by ARIMA,xgboost,RNN,LSTM,TCN
  ☆113Updated 5 years ago
• pyaf / load_forecasting
  Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models
  ☆572Updated 9 months ago
• Sanyam-Mehta / TPA-LSTM-PyTorch
  This is the PyTorch implementation of TPA-LSTM
  ☆61Updated 5 years ago
• IvanBongiorni / GAN-RNN_Timeseries-imputation
  Recurrent GAN for imputation of time series data. Implemented in TensorFlow 2 on Wikipedia Web Traffic Forecast dataset from Kaggle.
  ☆174Updated 2 years ago
• maxjcohen / transformer
  Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series.
  ☆890Updated 2 years ago