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PaperList with Code

Time Series Model

• A recurrent latent variable model for sequential data，NIPS 2015 [PDF] [Code]
• Sequential neural models with stochastic layers，NIPS 2016 [PDF] [Code]
• Structured Inference Networks for Nonlinear State Space Models，AAAI 2017 [PDF] [Code]
• Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series，AAAI 2020 [PDF] [Code]
• Multi-period Time Series Modeling with Sparsity via Bayesian Variational Inference，2017 [PDF] [Code]
• Unsupervised Scalable Representation Learning for Multivariate Time Series，NeurIPS 2019 [PDF] [Code]

Time series decomposition

• RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series，AAAI 2019 [PDF] [Code]
• RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering，IJCAI 2019 [PDF] [Code]

Prediction

• A Multi-Horizon Quantile Recurrent Forecaster，NIPS 2017 [PDF] [Code]
• DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks，2017 [PDF] [Code]
• Deep Factors for Forecasting，ICML 2019 [PDF] [Code]
• A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction，IJCAI 2017 [PDF] [Code]
• Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis，KDD 2018 [PDF] [Code]
• Deep State Space Models for Time Series Forecasting，NeurIPS 2018 [PDF] [Code]
• Explainable Deep Neural Networks for Multivariate Time Series Predictions，IJCAI 2019 [PDF] [Code]
• Modeling Extreme Events in Time Series Prediction，KDD 2018 [PDF] [Code]
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting，IJCAI 2019 [PDF] [Code]
• Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models，NeurIPS 2019 [PDF] [Code]
• Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting，NeurIPS 2019 [PDF] [Code]

Anomaly Detection

• LSTM-based encoder-decoder for multi-sensor anomaly detection，2016 [PDF] [Code]
• Anomaly Detection in Streams with Extreme Value Theory，KDD 2017 [PDF] [Code]
• Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network，KDD 2019 [PDF] [Code]
• Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding，KDD 2018 [PDF] [Code]
• A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data，AAAI 2019 [PDF] [Code]
• BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series，IJCAI 2019 [PDF] [Code]
• Deep Anomaly Detection with Deviation Networks，KDD 2019 [PDF] [Code]
• Deep autoencoding gaussian mixture model for unsupervised anomaly detection，ICLR 2018 [PDF] [Code]
• A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder，IEEE Robotics and Automation Letters 2018 [PDF] [Code]
• MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks，2019 [PDF] [Code]
• Outlier Detection for Time Series with Recurrent Autoencoder Ensembles，IJCAI 2019 [PDF] [Code]
• Sequential Anomaly Detection using Inverse Reinforcement Learning，KDD 2019 [PDF] [Code]
• Sparse Modeling-Based Sequential Ensemble Learning for Effective Outlier Detection in High-Dimensional Numeric Data，AAAI 2018 [PDF] [Code]
• Time-Series Anomaly Detection Service at Microsoft，KDD 2019 [PDF] [Code]
• Unsupervised Anomaly Detection for Intricate KPIs via Adversarial Training of VAE，INFOCOM 2019 [PDF] [Code]
• Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications，WWW 2018 [PDF] [Code]

Others

• Modeling Combinatorial Evolution in Time Series Prediction，2019 [PDF] [Code]
• SOM-VAE: Interpretable Discrete Representation Learning on Time Series，ICLR 2019 [PDF] [Code]
• Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps，2019 [PDF] [Code]
• Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets，AAAI 2020 [PDF] [Code]
• Multivariate Time Series Imputation with Variational Autoencoders，2019 [PDF] [Code]