List of state of the art papers focus on deep learning and resources, code and experiments using deep learning for time series forecasting. Classic methods vs Deep Learning methods.
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RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
- Qingsong Wen, et al.
- [Code]
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Constructing Gradient Controllable Recurrent Neural Networks Using Hamiltonian Dynamics
- Konstantin Rusch, et al.
- Code not yet.
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SOM-VAE: Interpretable Discrete Representation Learning on Time Series
ICLR 2019
- Vincent Fortuin, et al.
- [Code]
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Unsupervised Scalable Representation Learning for Multivariate Time Series
NeurIPS 2019
In Applications -- Time Series Analysis- Jean-Yves Franceschi, et al.
- [Code]
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Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series
- Zhi-Xuan Tan, et al.
- Code not yet.
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You May Not Need Order in Time Series Forecasting
- Yunkai Zhang, et al.
- Code not yet
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Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning
AAAI 2020
- Long H. Nguyen, et al.
- Code not yet
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Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
NeurIPS2019
- Vincent Le Guen and Nicolas Thome.
- [Code]
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Dynamic Local Regret for Non-convex Online Forecasting
NeurIPS 2019
- Sergul Aydore, et al.
- [Code]
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Bayesian Temporal Factorization for Multidimensional Time Series Prediction
- Xinyu Chen, and Lijun Sun
- [Code and data]
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Probabilistic sequential matrix factorization
- Ömer Deniz Akyildiz, et al.
- Code not yet
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Sequential VAE-LSTM for Anomaly Detection on Time Series
- Run-Qing Chen, et al.
- Code not yet
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High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes
NeurIPS 2019
- David Salinas, et al.
- Code not yet
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Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction
- Bryan Lim, et al.
- Code not yet
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- Chengxi Liu, et al.
- Code not yet
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SKTIME: A UNIFIED INTERFACE FOR MACHINE LEARNING WITH TIME SERIE
- [Code]
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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions
- [Code]
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- Antonio Rafael Sabino Parmezan, Vinicius M. A. Souza and Gustavo E. A. P. A. Batista. USP
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Explainable Deep Neural Networks for Multivariate Time Series Predictions
IJCAI 2019
- Roy Assaf and Anika Schumann. IBM Research, Zurich
- Code not yet
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Outlier Detection for Time Series with Recurrent Autoencoder Ensembles
IJCAI 2019
- [Code]
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Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting
IJCAI 2019
- Code not yet
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Deep Factors for Forecasting
ICML 2019
- Code not yet
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Probabilistic Forecasting with Spline Quantile Function RNNs
- Code not yet
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Deep learning for time series classification: a review
- Code not yet
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Multivariate LSTM-FCNs for Time Series Classification
- Code not yet
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Criteria for classifying forecasting methods
- Code not yet
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GluonTS: Probabilistic Time Series Models in Python
- [Code]
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DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
- David Salinas, et al.
- Code not yet
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Precision and Recall for Time Series
NeurIPS2018
- Nesime Tatbul, et al.
- Code not yet.
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Deep State Space Models for Time Series Forecasting
NeurIPS2018
- Code not yet
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Deep Factors with Gaussian Processes for Forecasting
Third workshop on Bayesian Deep Learning (NeurIPS 2018)
- [Code]
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DIFFUSION CONVOLUTIONAL RECURRENT NEURAL NETWORK: DATA-DRIVEN TRAFFIC FORECASTING
ICLR 2018
- Yaguang Li, et al.
- [Code]
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Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction
NIPS 2016
- [Code]
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Forecasting economic and financial time series: ARIMA VS. LSTM
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A comparative study between LSTM and ARIMA for sales forecasting in retail
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ARIMA/SARIMA vs LSTM with Ensemble learning Insights for Time Series Data
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Stock Market Prediction by Recurrent Neural Network on LSTM Model
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Decoupling Hierarchical Recurrent Neural Networks With Locally Computable Losses