Alternatives and similar repositories for deepTCN:

Users that are interested in deepTCN are comparing it to the libraries listed below

• Vsooong / multivariate-prediction
  time-series-predictoin(LSTNet,SAB,Transformer...)
  ☆138Updated 6 years ago
• amitesh863 / nbeats_forecast
  N-Beats library implementation
  ☆87Updated 3 years ago
• albertogaspar / dts
  A Keras library for multi-step time-series forecasting.
  ☆176Updated last year
• sunfanyunn / DARNN
  A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
  ☆111Updated 6 months ago
• Maple728 / MTNet
  Tensorflow implementation of paper http://arxiv.org/abs/1809.02105
  ☆66Updated 5 years ago
• Lorne0 / LSTNet_keras
  Keras version of LSTNet
  ☆97Updated 5 years ago
• tianchen101 / MQRNN
  Multi-Quantile Recurrent Neural Network for Quantile Regression
  ☆63Updated 4 years ago
• bighuang624 / DSANet
  Code for the CIKM 2019 paper "DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting".
  ☆261Updated 7 months ago
• pedrolarben / TimeSeriesForecasting-DeepLearning
  An experiemtal review on deep learning architectures for time series forecasting
  ☆139Updated 3 years ago
• smallGum / MLCNN-Multivariate-Time-Series
  Codes for Multi-Level Construal Neural Network framework
  ☆49Updated 4 years ago
• fengyang95 / Awesome-Deep-Learning-Based-Time-Series-Forecasting
  ☆146Updated 3 years ago
• fbadine / LSTNet
  A Tensorflow / Keras implementation of "Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks" paper
  ☆172Updated 2 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.
  ☆170Updated 2 years ago
• arleigh418 / Paper-Implementation-DSTP-RNN-For-Stock-Prediction-Based-On-DA-RNN
  基於DA-RNN之DSTP-RNN論文試做(Ver1.0)
  ☆77Updated 4 years ago
• chensvm / A-Dual-Stage-Attention-Based-Recurrent-Neural-Network-for-Time-Series-Prediction
  ☆116Updated 6 years ago
• cchallu / nbeatsx
  ☆193Updated 2 years ago
• vincent-leguen / STRIPE
  Code for our NeurIPS 2020 paper "Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity"
  ☆87Updated 3 years ago
• abis330 / PredAir
  Forecasting air pollution using temporal attention mechanism in Beijing
  ☆47Updated 4 years ago
• KurochkinAlexey / DA-RNN
  Pytorch implementation of Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction https://arxiv.org/pdf/1704.02971…
  ☆37Updated 5 years ago
• drzhang3 / DeepTimeSeriesModel
  A paper list for Time series modelling, including prediciton and anomaly detection
  ☆91Updated 4 years ago
• Sanyam-Mehta / TPA-LSTM-PyTorch
  This is the PyTorch implementation of TPA-LSTM
  ☆58Updated 5 years ago
• rajatsen91 / deepglo
  This repository contains code for the paper: https://arxiv.org/abs/1905.03806. It also contains scripts to reproduce the results in the p…
  ☆168Updated 4 years ago
• spallas / time-attention
  Implementation of RNN for Time Series prediction from the paper https://arxiv.org/abs/1704.02971
  ☆60Updated 2 years ago
• lpphd / multivariate-attention-tcn
  ☆90Updated last year
• HansikaPH / time-series-forecasting
  Recurrent Neural Network Implementations for Time Series Forecasting
  ☆73Updated 3 years ago
• Daniela-Shereen / GBRT-for-TSF
  ☆197Updated last year
• flaviagiammarino / deep-tcn-tensorflow
  TensorFlow implementation of DeepTCN model for probabilistic time series forecasting with temporal convolutional networks.
  ☆38Updated 9 months ago
• PsiPhiTheta / LSTM-Attention
  A Comparison of LSTMs and Attention Mechanisms for Forecasting Financial Time Series
  ☆72Updated 6 years ago
• KurochkinAlexey / Hierarchical-Attention-Based-Recurrent-Highway-Networks-for-Time-Series-Prediction
  Pytorch implementation of Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction https://arxiv.org/abs/1806.0…
  ☆63Updated 5 years ago
• shashwattrivedi / TimeSeriesTransformerLSTM
  ☆26Updated 5 years ago