This README serves as a quick start guide to deploy Zscaler Cloud Connector resources in an AWS cloud using AWS CloudFormation templates. To learn more about the resources created when deploying Cloud Connector with CloudFormation, see Deployment Templates for Zscaler Cloud Connector.

Use this repository to create the deployment resources required to deploy and operate Cloud Connector in an existing virtual private cloud (VPC). To learn more about the steps for deploying Cloud Connector using a CloudFormation template, see Deploying Cloud Connector with Amazon Web Services.

Before deploying your Cloud Connectors using CloudFormation, you must upload the Pre-Deployment Template to the AWS CloudFormation console to create a Pre-Deployment Template Stack.

Use the Starter Deployment Template to create the resources needed to deploy and operate Cloud Connector in an existing VPC.

Use the Add-on Template with GWLB to distribute traffic across multiple Cloud Connectors and achieve high availability. Zscaler’s recommended deployment method is Gateway Load Balancer (GWLB). This add-on is only compatible with Starter Deployment Template as Auto Scaling deployments include GWLB automatically.

Use the Add-on Template with ZPA to create the resources needed to enable the ZPA DNS resolver capability on Cloud Connector in an existing VPC.

Use the Starter Deployment Template with Auto Scaling and Gateway Load Balancer (GWLB) to create the resources needed to deploy and operate Cloud Connector in an existing VPC and load balance traffic across multiple Cloud Connectors. Zscaler’s recommended deployment method is Gateway Load Balancer (GWLB). GWLB distributes traffic across multiple Cloud Connectors and achieves high availability. For added resiliency and elasticity, Cloud Connectors are deployed via a Launch Template configured Auto Scaling group.

Use the Add-on Template with ZPA to create the resources needed to enable the ZPA DNS resolver capability on Cloud Connector in an existing VPC.

A collection of premade features for nbb

This repository aims to make it easy to use libraries that nbb is not
currently able to evaluate through SCI. This
repository does this by providing premade features/libraries that can be pulled
in to a custom build of nbb.

Use the features in this repository to build a custom nbb. Here are the steps for doing this:

1. In a new project, copy this repository’s bb.edn and package.json files.
   • NOTE: The :git/sha for nbb/nbb may be old. You may want to update it to build the most recent version of nbb.
2. In package.json, rename the nbb-features references to make them your own.
3. In bb.edn, leave the nbb dependencies as those are needed for building nbb. As for the other dependencies, replace them with the features you want enabled. If the feature is local, use :local/root as seen in the copied bb.edn. If using a feature from this remote repository, pull them in like this:

:deps
{datascript/deps
 {:git/url "https://github.com/babashka/nbb-features"
  :git/sha "7af8569a7f932af2cde5f8677133915540ab0c49"
  :deps/root "features/datascript"}
 }

4. Build your custom nbb with bb release. Run node lib/nbb_main.js and your nbb with custom features enabled will run!

The following libraries are provided as features:

• https://github.com/tonsky/datascript
• https://github.com/tonsky/datascript-transit
• https://github.com/frankiesardo/linked

NOTE: These features usually provide a subset of a library’s API to nbb. To see what library fns are available, see the feature’s sci configuration.

To contribute a new feature, please add a new feature under features/ and corresponding tests under test/features/. See this doc for what’s required to make a feature. Before contributing a feature, make sure that the library doesn’t already work with nbb and SCI. If your nbb feature was useful to you, we’d love to have it as a contribution!

• https://github.com/logseq/nbb-logseq

Distributed under the EPL License. See LICENSE.

Este es un proyecto personal y privado alojado en un repositorio de mi cuenta de GitHub. Lo creé para mantener actualizadas mis diferentes computadoras, tanto físicas como virtuales, con mis herramientas de utilidad más comunes. Es el primer proyecto donde aplico técnicas básicas de CI/CD con GitHub Actions.

A pesar de que es un proyecto privado, alguno de mis colaboradores y amigos me han solicitado acceso al mismo para su uso. Mediante este sitio web, estoy compartiendo una parte de mis utilitarios no sensibles para su uso por otros y no limitado a mis colaboradores y amigos.

Existen dos (2) formas de despliegue de uso:

• Usuario: Se instala mediante este sitio y con las herramientas no sensibles y de uso general
• Devops: Se requiere un token o acceso ssh al proyecto dado por mí a mis colaboradores de PANAMATECH ONLINE u otros colaboradores de programación.

Desde este enlace, descargue el archivo install_bin.sh. Regularmente o en mi caso va a la carpeta de descargas.

Si esto es así (carpeta de descargas), muevalo a la carpeta /tmp:

$ mv $HOME/Descargas/install_bin.sh /tmp

y realice los siguietes pasos:

$ chmod a+x /tmp/install_bin.sh
$ /tmp/install_bin.sh
$ rm /tmp/install_bin.sh

o directamente desde la consola, ejecute esto:

$ wget -O - https://maurorosero.github.io/bin/scripts/install_bin.sh | bash

En este punto, le va a pedir su cpntraseña y si es correcta iniciará el proceso de instalación directamente. Una vez terminado, se debe haber creado la carpeta bin dentro de la carpeta HOME de su usuario.

Información reservada solo para colaboradores de PANAMATECH ONLINE u otros colaboradores de programación.

Es una colección de programas python, script en bash, librerías, y otras variantes de funciones ejecutables diseñadas, especialmente, para funcionar desde la línea de comandos (shell):

Main purpouse of application is keeping item.dbc up to date with database item data.

Supports:

• Client – 3.3.5 (in theory should work for everything between 3.0.3 and 5.4.7 though)
• Emu – TC2 release 335.63 (and probably most of if not all previous releases, and most of if not all of other emus as well, as long as they use MySQL)

Provide application with your Item.dbc, fill in connection information. For most of users leaving StartID and EndID in default values and leaving all 3 options mentioned below (next to DO STUFF!!! button in UI) checked will be fine.

You can choose range of IDs tool will work with, in case you (for whatever reason) don’t want it to work with all items in existence. You can use Test connection to check wheter connection information you provided is OK. You can use Save settings to make your current settings be default on next app’s startup. You can also use Check for changes, in case you want to know how many mismatches between database and DBC currently exist in given range of IDs.

You can also choose which operations are to be done next to DO STUFF!!! button.:

• Create new in DB – Items in DBC, which are missing in DB, will be inserted into DB.
• Create new in DBC – Items in DB, which are missing in DBC, will be inserted into DBC.
• Update existing in DBC – Items in DBC, which don’t match their DB counterparts, will get updated.

This part can be ignored by everyone who doesn’t have compatibility issues with his emulator’s database, or who isn’t unhappy with default values set to items generated by this tool.

If you want to change the way application behaves when generating items from DBC without touching actual code, you need to modify SQLConfig.xml. In ColAssociations part, do NOT edit names of elements placed there. Only change names of columns (inner texts (content) of elements) in case your emu is not compatible with tool because of mismatching column names in database.

You can also change values and add elements under element DefaultValues. For every single element in this part, value provided in its inner text will be inserted into field specified by element’s name. By default, only Name of generated item is set (ItemSyncGeneratedItem is used), other item values in database are left to be set by default values in table’s structure.

A professionally curated list of papers (with available code), tutorials, and surveys on recent AI for Time Series Analysis (AI4TS), including Time Series, Spatio-Temporal Data, Event Data, Sequence Data, Temporal Point Processes, etc., at the Top AI Conferences and Journals, which is updated ASAP (the earliest time) once the accepted papers are announced in the corresponding top AI conferences/journals.

The top conferences including:

• Machine Learning: NeurIPS, ICML, ICLR
• Data Mining: KDD
• Artificial Intelligence: AAAI, IJCAI
• Data Management: SIGMOD, VLDB, ICDE
• Misc (selected): WWW, AISTAT, CIKM, ICDM, WSDM, SIGIR, ICASSP, CVPR, ICCV, etc.

The top journals including (mainly for survey papers): CACM, PIEEE, TPAMI, TKDE, TNNLS, TITS, TIST, SPM, JMLR, JAIR, CSUR, DMKD, KAIS, IJF, arXiv(selected), etc.

This list is also simultaneously updated in the Repo AI4TS Paper.

• [2022-06-02] Add papers accepted by ICML’22, ICLR’22, AAAI’22, IJCAI’22!

• AI4TS Tutorials and Surveys

  • AI4TS Tutorials
  • AI4TS Surveys

• AI4TS Papers 2022

  • NeurIPS 2022, ICML 2022, ICLR 2022
  • KDD 2022, AAAI 2022, IJCAI 2022
  • SIGMOD VLDB ICDE 2022
  • Misc 2022

• AI4TS Papers 2021

  • NeurIPS 2021, ICML 2021, ICLR 2021
  • KDD 2021, AAAI 2021, IJCAI 2021
  • SIGMOD VLDB ICDE 2021
  • Misc 2021

• AI4TS Papers 201X-2020 Selected

  • NeurIPS 201X-2020, ICML 201X-2020, ICLR 201X-2020
  • KDD 201X-2020, AAAI 201X-2020, IJCAI 201X-2020
  • SIGMOD VLDB ICDE 201X-2020
  • Misc 201X-2020

• Robust Time Series Analysis and Applications: An Industrial Perspective, in KDD 2022. [Link]
• Time Series in Healthcare: Challenges and Solutions, in AAAI 2022. [Link]
• Robust Time Series Analysis: from Theory to Applications in the AI Era, in IJCAI 2022. [Link]
• Time Series Anomaly Detection: Tools, Techniques and Tricks, in DASFAA 2022. [Link]
• Modern Aspects of Big Time Series Forecasting, in IJCAI 2021. [Link]
• Explainable AI for Societal Event Predictions: Foundations, Methods, and Applications, in AAAI 2021. [Link]
• Physics-Guided AI for Large-Scale Spatiotemporal Data, in KDD 2021. [Link]
• Building Forecasting Solutions Using Open-Source and Azure Machine Learning, in KDD 2020. [Link]
• Interpreting and Explaining Deep Neural Networks: A Perspective on Time Series Data, KDD 2020. [Link]
• Forecasting Big Time Series: Theory and Practice, KDD 2019. [Link]
• Spatio-Temporal Event Forecasting and Precursor Identification, KDD 2019. [Link]
• Modeling and Applications for Temporal Point Processes, KDD 2019. [Link] [Link2]

• Time series data augmentation for deep learning: a survey, in IJCAI 2021. [paper]
• Neural temporal point processes: a review, in IJCAI 2021. [paper]
• Causal inference for time series analysis: problems, methods and evaluation, in KAIS 2022. [paper]
• Survey and Evaluation of Causal Discovery Methods for Time Series, in JAIR 2022. [paper]
• Deep learning for spatio-temporal data mining: A survey, in TKDE 2020. [paper]
• Generative Adversarial Networks for Spatio-temporal Data: A Survey, in TIST 2022. [paper]
• Spatio-Temporal Data Mining: A Survey of Problems and Methods, in CSUR 2018. [paper]
• A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series, in NeurIPS Workshop 2020. [paper]
• A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data, in arXiv 2020. [paper]
• Transformers in Time Series: A Survey, in arXiv 2022. [paper]

• Forecasting: theory and practice, in IJF 2022. [paper]
• Time-series forecasting with deep learning: a survey, in Philosophical Transactions of the Royal Society A 2021. [paper]
• Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions, in TITS 2022. [paper]
• Event prediction in the big data era: A systematic survey, in CSUR 2022. [paper]
• A brief history of forecasting competitions, in IJF 2020. [paper]
• Neural forecasting: Introduction and literature overview, in arXiv 2020. [paper]

• A review on outlier/anomaly detection in time series data, in CSUR 2021. [paper]
• Anomaly detection for IoT time-series data: A survey, in IEEE Internet of Things Journal 2019. [paper]
• A Survey of AIOps Methods for Failure Management, in TIST 2021. [paper]
• Sequential (quickest) change detection: Classical results and new directions, in IEEE Journal on Selected Areas in Information Theory 2021. [paper]
• Anomaly detection for discrete sequences: A survey, TKDE’12. [paper]

• Deep learning for time series classification: a review, in Data Mining and Knowledge Discovery 2019. [paper]
• Approaches and Applications of Early Classification of Time Series: A Review, in IEEE Transactions on Artificial Intelligence 2020. [paper]

Not yet announced

• FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting [paper] [official code]
• TACTiS: Transformer-Attentional Copulas for Time Series [paper]
• Domain Adaptation for Time Series Forecasting via Attention Sharing [paper]
• Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
• DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting

• Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection

• Adaptive Conformal Predictions for Time Series [paper] [official code]
• Modeling Irregular Time Series with Continuous Recurrent Units [paper]
• Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion [paper]
• Reconstructing nonlinear dynamical systems from multi-modal time series [paper]
• Utilizing Expert Features for Contrastive Learning of Time-Series Representations
• Learning of Cluster-based Feature Importance for Electronic Health Record Time-series

• Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting [paper] [official code]
• DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting [paper] [official code]
• CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting [paper] [official code]
• Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift [paper] [official code]
• TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting [paper] [official code]
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [paper] [official code]
• On the benefits of maximum likelihood estimation for Regression and Forecasting [paper]
• Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting [paper] [official code]

• Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy [paper] [official code]
• Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series [paper] [official code]

• T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis [paper]
• Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification [paper]

• Graph-Guided Network for Irregularly Sampled Multivariate Time Series [paper]
• Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series [paper]
• Transformer Embeddings of Irregularly Spaced Events and Their Participants [paper]
• Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks [paper]
• PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series [paper]
• Huber Additive Models for Non-stationary Time Series Analysis [paper]
• LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations [paper]
• Imbedding Deep Neural Networks [paper]
• Coherence-based Label Propagation over Time Series for Accelerated Active Learning [paper]
• Long Expressive Memory for Sequence Modeling [paper]
• Autoregressive Quantile Flows for Predictive Uncertainty Estimation [paper]
• Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks [paper]
• Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification [paper]
• Explaining Point Processes by Learning Interpretable Temporal Logic Rules [paper]

Not yet announced

• CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting [paper]
• Reinforcement Learning based Dynamic Model Combination for Time Series Forecasting [paper]
• PrEF: Probabilistic Electricity Forecasting via Copula-Augmented State Space Model [paper]
• LIMREF: Local Interpretable Model Agnostic Rule-Based Explanations for Forecasting, with an Application to Electricity Smart Meter Data [paper]
• Learning and Dynamical Models for Sub-Seasonal Climate Forecasting: Comparison and Collaboration [paper] [official code]
• CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting [paper]
• Conditional Local Convolution for Spatio-Temporal Meteorological Forecasting [paper] [official code]
• Graph Neural Controlled Differential Equations for Traffic Forecasting [paper] [official code]
• STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction [paper] [official code]

• Towards a Rigorous Evaluation of Time-Series Anomaly Detection [paper]
• AnomalyKiTS-Anomaly Detection Toolkit for Time Series [Demo paper]

• TS2Vec: Towards Universal Representation of Time Series [paper] [official code]
• I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding [paper]
• Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis [paper]
• Conditional Loss and Deep Euler Scheme for Time Series Generation [paper]
• Clustering Interval-Censored Time-Series for Disease Phenotyping [paper]

• Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting [paper]
• Coherent Probabilistic Aggregate Queries on Long-horizon Forecasts [paper] [official code]
• Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting
• DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data [paper] [official code]
• Memory Augmented State Space Model for Time Series Forecasting
• Physics-Informed Long-Sequence Forecasting From Multi-Resolution Spatiotemporal Data
• Long-term Spatio-Temporal Forecasting via Dynamic Multiple-Graph Attention [paper] [official code]
• FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting

• Neural Contextual Anomaly Detection for Time Series [paper]
• GRELEN: Multivariate Time Series Anomaly Detection from the Perspective of Graph Relational Learning

• A Reinforcement Learning-Informed Pattern Mining Framework for Multivariate Time Series Classification [paper]
• T-SMOTE: Temporal-oriented Synthetic Minority Oversampling Technique for Imbalanced Time Series Classification

• METRO: A Generic Graph Neural Network Framework for Multivariate Time Series Forecasting, VLDB’22. [paper] [official code]
• AutoCTS: Automated Correlated Time Series Forecasting, VLDB’22. [paper]
• Towards Spatio-Temporal Aware Traffic Time Series Forecasting, ICDE’22. [paper] [official code]

• Sintel: A Machine Learning Framework to Extract Insights from Signals, SIGMOD’22. [paper] [official code]
• TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection, VLDB’22. [paper] [official code]
• TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data, VLDB’22. [paper] [official code]
• Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles, VLDB’22. [paper]
• Robust and Explainable Autoencoders for Time Series Outlier Detection, ICDE’22. [paper]
• Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders, ICDE’22.

• IPS: Instance Profile for Shapelet Discovery for Time Series Classification, ICDE’22. [paper]
• Towards Backdoor Attack on Deep Learning based Time Series Classification, ICDE’22. [paper]

• OnlineSTL: Scaling Time Series Decomposition by 100x, VLDB’22. [paper]
• Efficient temporal pattern mining in big time series using mutual information, VLDB’22. [paper]
• Learning Evolvable Time-series Shapelets, ICDE’22.

• CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting, WWW’22. [paper] [official code]
• Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction, WWW’22. [paper]
• RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph, WWW’22. [paper]
• Robust Probabilistic Time Series Forecasting, AISTATS’22. [paper] [official code]
• Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting, AISTATS’22. [paper]

• Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection, AISTATS’22. [paper] [official code]
• A Semi-Supervised VAE Based Active Anomaly Detection Framework in Multivariate Time Series for Online Systems, WWW’22. [paper]

• Decoupling Local and Global Representations of Time Series, AISTATS’22. [paper] [official code]
• LIMESegment: Meaningful, Realistic Time Series Explanations, AISTATS’22. [paper]
• Using time-series privileged information for provably efficient learning of prediction models, AISTATS’22. [paper] [official code]
• Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation, AISTATS’22. [paper] [official code]
• EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting, WWW’22. [paper]

• Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting [paper] [official code]
• MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data [paper]
• Conformal Time-Series Forecasting [paper] [official code]
• Probabilistic Forecasting: A Level-Set Approach [paper]
• Topological Attention for Time Series Forecasting [paper]
• When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting [paper] [official code]
• Monash Time Series Forecasting Archive [paper] [official code]

• Revisiting Time Series Outlier Detection: Definitions and Benchmarks [paper] [official code]
• Online false discovery rate control for anomaly detection in time series [paper]
• Detecting Anomalous Event Sequences with Temporal Point Processes [paper]

• Probabilistic Transformer For Time Series Analysis [paper]
• Shifted Chunk Transformer for Spatio-Temporal Representational Learning [paper]
• Deep Explicit Duration Switching Models for Time Series [paper] [official code]
• Time-series Generation by Contrastive Imitation [paper]
• CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [paper] [official code]
• Adjusting for Autocorrelated Errors in Neural Networks for Time Series [paper] [official code]
• SSMF: Shifting Seasonal Matrix Factorization [paper] [official code]
• Coresets for Time Series Clustering [paper]
• Neural Flows: Efficient Alternative to Neural ODEs [paper] [official code]
• Spatio-Temporal Variational Gaussian Processes [paper] [official code]
• Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers [paper] [official code]

• Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting [paper] [official code]
• End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series [paper] [official code]
• RNN with particle flow for probabilistic spatio-temporal forecasting [paper] [official code]
• Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting [paper] [official code]
• Variance Reduction in Training Forecasting Models with Subgroup Sampling [paper]
• Explaining Time Series Predictions With Dynamic Masks [paper] [official code]
• Conformal prediction interval for dynamic time-series [paper] [official code]

• Neural Transformation Learning for Deep Anomaly Detection Beyond Images [paper] [official code]
• Event Outlier Detection in Continuous Time [paper] [official code]

• Voice2Series: Reprogramming Acoustic Models for Time Series Classification [paper] [official code]
• Neural Rough Differential Equations for Long Time Series [paper] [official code]
• Neural Spatio-Temporal Point Processes [paper] [official code]
• Learning Neural Event Functions for Ordinary Differential Equations [paper] [official code]
• Approximation Theory of Convolutional Architectures for Time Series Modelling [paper]
• Whittle Networks: A Deep Likelihood Model for Time Series [paper] [official code]
• Necessary and sufficient conditions for causal feature selection in time series with latent common causes [paper]

• Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows [paper] [official code]
• Discrete Graph Structure Learning for Forecasting Multiple Time Series [paper] [official code]

• Clairvoyance: A Pipeline Toolkit for Medical Time Series [paper] [official code]
• Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding [paper] [official code]
• Multi-Time Attention Networks for Irregularly Sampled Time Series [paper] [official code]
• Generative Time-series Modeling with Fourier Flows [paper] [official code]
• Neural ODE Processes [paper] [official code]
• Learning Continuous-Time Dynamics by Stochastic Differential Networks [paper] [official code]

• ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting [paper] [official code]
• Graph Deep Factors for Forecasting with Applications to Cloud Resource Allocation [paper]
• Quantifying Uncertainty in Deep Spatiotemporal Forecasting [paper]
• Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting [paper] [official code]
• TrajNet: A Trajectory-Based Deep Learning Model for Traffic Prediction [paper]
• Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting [paper]

• Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding [paper] [official code]
• Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization [paper] [official code]
• Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering [paper] [official code]
• Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection [paper] [official code]

• Representation Learning of Multivariate Time Series using a Transformer Framework [paper] [official code]
• Causal and Interpretable Rules for Time Series Analysis [paper]
• MiniRocket: A Fast (Almost) Deterministic Transform for Time Series Classification [paper] [official code]
• Statistical models coupling allows for complex localmultivariate time series analysis [paper]
• Fast and Accurate Partial Fourier Transform for Time Series Data [paper] [official code]
• Deep Learning Embeddings for Data Series Similarity Search [paper] [link]

• Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting [paper] [official code]
• Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting [paper] [official code]
• Dynamic Gaussian Mixture Based Deep Generative Model for Robust Forecasting on Sparse Multivariate Time Series [paper] [official code]
• Temporal Latent Autoencoder: A Method for Probabilistic Multivariate Time Series Forecasting [paper]
• Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting [paper]
• Meta-Learning Framework with Applications to Zero-Shot Time-Series Forecasting [paper]
• Attentive Neural Point Processes for Event Forecasting [paper] [official code]
• Forecasting Reservoir Inflow via Recurrent Neural ODEs [paper]
• Hierarchical Graph Convolution Network for Traffic Forecasting [paper]
• Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network [paper] [official code]
• Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting [paper] [official code]
• FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting [paper] [official code]
• Fairness in Forecasting and Learning Linear Dynamical Systems [paper]
• A Multi-Step-Ahead Markov Conditional Forward Model with Cube Perturbations for Extreme Weather Forecasting [paper]
• Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances [paper]

• Graph Neural Network-Based Anomaly Detection in Multivariate Time Series [paper] [official code]
• Time Series Anomaly Detection with Multiresolution Ensemble Decoding [paper]
• Outlier Impact Characterization for Time Series Data [paper]

• Correlative Channel-Aware Fusion for Multi-View Time Series Classification [paper]
• Learnable Dynamic Temporal Pooling for Time Series Classification [paper] [official code]
• ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification [paper]
• Joint-Label Learning by Dual Augmentation for Time Series Classification [paper]

• Time Series Domain Adaptation via Sparse Associative Structure Alignment [paper] [official code]
• Learning Representations for Incomplete Time Series Clustering [paper]
• Generative Semi-Supervised Learning for Multivariate Time Series Imputation [paper] [official code]
• Second Order Techniques for Learning Time-Series with Structural Breaks [paper]

• Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting [paper]
• Residential Electric Load Forecasting via Attentive Transfer of Graph Neural Networks [paper]
• Hierarchical Adaptive Temporal-Relational Modeling for Stock Trend Prediction [paper]
• TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning [paper] [official code]

• Time Series Data Augmentation for Deep Learning: A Survey [paper]
• Time-Series Representation Learning via Temporal and Contextual Contrasting [paper] [official code]
• Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation [paper] [official code]
• Time-Aware Multi-Scale RNNs for Time Series Modeling [paper]
• TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data [paper]

• AutoAI-TS:AutoAI for Time Series Forecasting, SIGMOD’21. [paper]
• FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data, VLDB’21. [paper]
• MDTP: a multi-source deep traffic prediction framework over spatio-temporal trajectory data, VLDB’21. [paper]
• EnhanceNet: Plugin Neural Networks for Enhancing Correlated Time Series Forecasting, ICDE’21. [paper] [slides]
• An Effective Joint Prediction Model for Travel Demands and Traffic Flows, ICDE’21. [paper]

• Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series, VLDB’21. [paper] [official code]
• SAND: Streaming Subsequence Anomaly Detection, VLDB’21. [paper]

• RobustPeriod: Robust Time-Frequency Mining for Multiple Periodicity Detection, SIGMOD’21. [paper] [code]
• ORBITS: Online Recovery of Missing Values in Multiple Time Series Streams, VLDB’21. [paper] [official code]
• Missing Value Imputation on Multidimensional Time Series, VLDB’21. [paper]

• DeepFEC: Energy Consumption Prediction under Real-World Driving Conditions for Smart Cities, WWW’21. [paper] [official code]
• AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph, WWW’21. [paper] [official code]
• REST: Reciprocal Framework for Spatiotemporal-coupled Predictions, WWW’21. [paper]
• Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series, AISTATS’21. [paper]
• SSDNet: State Space Decomposition Neural Network for Time Series Forecasting, ICDM’21. [paper]
• AdaRNN: Adaptive Learning and Forecasting of Time Series, CIKM’21. [paper] [official code]
• Learning to Learn the Future: Modeling Concept Drifts in Time Series Prediction, CIKM’21. [paper]
• Stock Trend Prediction with Multi-Granularity Data: A Contrastive Learning Approach with Adaptive Fusion, CIKM’21. [paper]
• DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction, CIKM’21. [paper] [official code1] [official code2]
• Long Horizon Forecasting With Temporal Point Processes, WSDM’21. [paper] [official code]
• Time-Series Event Prediction with Evolutionary State Graph, WSDM’21. [paper] [official code].

• SDFVAE: Static and Dynamic Factorized VAE for Anomaly Detection of Multivariate CDN KPIs, WWW’21. [paper]
• Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding, WWW’21. [paper] [official code]
• FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection, WSDM’21. [paper]
• Weakly Supervised Temporal Anomaly Segmentation with Dynamic Time Warping, ICCV’21. [paper] [official code]

• Network of Tensor Time Series, WWW’21. [paper] [official code]
• Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series, WWW’21. [paper] [official code]
• SrVARM: State Regularized Vector Autoregressive Model for Joint Learning of Hidden State Transitions and State-Dependent Inter-Variable Dependencies from Multi-variate Time Series, WWW’21. [paper]
• Deep Fourier Kernel for Self-Attentive Point Processes, AISTATS’21. [paper]
• Differentiable Divergences Between Time Series, AISTATS’21. [paper] [official code]
• Aligning Time Series on Incomparable Spaces, AISTATS’21. [paper] [official code]
• Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions, ICDM’21. [paper]
• Towards Generating Real-World Time Series Data, ICDM’21. [paper] [official code]
• Learning Saliency Maps to Explain Deep Time Series Classifiers, CIKM’21. [paper] [official code]
• Actionable Insights in Urban Multivariate Time-series, CIKM’21. [paper]
• Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals, WSDM’21. [paper]

• Adversarial Sparse Transformer for Time Series Forecasting, NeurIPS’20. [paper] [official code]
• Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting, NeurIPS’20. [paper] [official code]
• Deep Rao-Blackwellised Particle Filters for Time Series Forecasting, NeurIPS’20. [paper]
• Probabilistic Time Series Forecasting with Shape and Temporal Diversity, NeurIPS’20. [paper] [official code]
• Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting, NeurIPS’20. [paper] [official code]
• Interpretable Sequence Learning for Covid-19 Forecasting, NeurIPS’20. [paper]
• Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting, NeurIPS’19. [paper] [code]
• Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting, NeurIPS’19. [paper] [official code]
• High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes, NeurIPS’19. [paper] [official code]
• Deep State Space Models for Time Series Forecasting, NeurIPS’18. [paper]
• Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction, NeurIPS’16. [paper]

• Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network, NeurIPS’20. [paper]
• PIDForest: Anomaly Detection via Partial Identification, NeurIPS’19. [paper] [official code]
• Precision and Recall for Time Series, NeurIPS’18. [paper] [official code]

• Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices, NeurIPS’19. [paper]

• Learning Representations for Time Series Clustering, NeurIPS’19. [paper] [official code]
• Learning low-dimensional state embeddings and metastable clusters from time series data, NeurIPS’19. [paper]

• NAOMI: Non-autoregressive multiresolution sequence imputation, NeurIPS’19. [paper] [official code]
• BRITS: Bidirectional Recurrent Imputation for Time Series, NeurIPS’18. [paper] [official code]
• Multivariate Time Series Imputation with Generative Adversarial Networks, NeurIPS’18. [paper] [official code]

• Neural Controlled Differential Equations for Irregular Time Series, NeurIPS’20. [paper] [official code]
• GRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series, NeurIPS’19. [paper] [official code]
• Latent Ordinary Differential Equations for Irregularly-Sampled Time Series, NeurIPS’19. [paper] [official code]
• Neural Ordinary Differential Equations, NeurIPS’18. [paper] [official code]

• High-recall causal discovery for autocorrelated time series with latent confounders, NeurIPS’20. [paper] [paper2] [official code]
• Benchmarking Deep Learning Interpretability in Time Series Predictions, NeurIPS’20. [paper] [official code]
• What went wrong and when? Instance-wise feature importance for time-series black-box models, NeurIPS’20. [paper] [official code]
• Normalizing Kalman Filters for Multivariate Time Series Analysis, NeurIPS’20. [paper]
• Unsupervised Scalable Representation Learning for Multivariate Time Series, NeurIPS’19. [paper] [official code]
• Time-series Generative Adversarial Networks, NeurIPS’19. [paper] [official code]
• U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging, NeurIPS’19. [paper] [official code]
• Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders, NeurIPS’18. [paper]
• Safe Active Learning for Time-Series Modeling with Gaussian Processes, NeurIPS’18. [paper]

• Learning from Irregularly-Sampled Time Series: A Missing Data Perspective, ICML’20. [paper] [official code]
• Set Functions for Time Series, ICML’20. [paper] [official code]
• Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders, ICML’20. [paper] [official code]
• Spectral Subsampling MCMC for Stationary Time Series, ICML’20. [paper]
• Learnable Group Transform For Time-Series, ICML’20. [paper]
• Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models, ICML’19. [paper] [official code]
• Discovering Latent Covariance Structures for Multiple Time Series, ICML’19. [paper]
• Autoregressive convolutional neural networks for asynchronous time series, ICML’18. [paper] [official code]
• Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series, ICML’18. [paper]
• Soft-DTW: a Differentiable Loss Function for Time-Series, ICML’17. [paper] [official code]

• Forecasting Sequential Data Using Consistent Koopman Autoencoders, ICML’20. [paper] [official code]
• Adversarial Attacks on Probabilistic Autoregressive Forecasting Models, ICML’20. [paper] [official code]
• Influenza Forecasting Framework based on Gaussian Processes, ICML’20. [paper]
• Deep Factors for Forecasting, ICML’19. [paper]
• Coherent Probabilistic Forecasts for Hierarchical Time Series, ICML’17. [paper]

• Interpolation-Prediction Networks for Irregularly Sampled Time Series, ICLR’19. [paper] [official code]
• SOM-VAE: Interpretable Discrete Representation Learning on Time Series, ICLR’19. [paper] [official code]

• N-BEATS: Neural basis expansion analysis for interpretable time series forecasting, ICLR’20. [paper] [official code]
• Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting, ICLR’18. [paper] [official code]
• Automatically Inferring Data Quality for Spatiotemporal Forecasting, ICLR’18. [paper]

• Fast RobustSTL: Efficient and Robust Seasonal-Trend Decomposition for Time Series with Complex Patterns, KDD’20. [paper] [code]
• Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data, KDD’20. [paper] [official code]
• Online Amnestic DTW to allow Real-Time Golden Batch Monitoring, KDD’19. [paper]
• Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis, KDD’18. [paper]
• Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data, KDD’17. [paper]

• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks, KDD’20. [paper] [official code]
• Attention based Multi-Modal New Product Sales Time-series Forecasting, KDD’20. [paper]
• Forecasting the Evolution of Hydropower Generation, KDD’20. [paper]
• Modeling Extreme Events in Time Series Prediction, KDD’19. [paper]
• Multi-Horizon Time Series Forecasting with Temporal Attention Learning, KDD’19. [paper]
• Regularized Regression for Hierarchical Forecasting Without Unbiasedness Conditions, KDD’19. [paper]
• Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units, KDD’19. [paper] [official code]
• Dynamic Modeling and Forecasting of Time-evolving Data Streams, KDD’19. [paper] [official code]
• DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events, KDD’19. [paper] [official code]
• Stock Price Prediction via Discovering Multi-Frequency Trading Patterns, KDD’17. [paper] [official code]

• USAD: UnSupervised Anomaly Detection on Multivariate Time Series, KDD’20. [paper] [official code]
• RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks, KDD’20 MiLeTS. [paper]
• Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network, KDD’19. [paper] [official code]
• Time-Series Anomaly Detection Service at Microsoft, KDD’19. [paper]
• Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding, KDD’18. [paper] [official code]
• Anomaly Detection in Streams with Extreme Value Theory, KDD’17. [paper]

• Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets, AAAI’20. [paper] [official code]
• DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series, AAAI’20. [paper]
• Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series, AAAI’20. [paper] [official code]
• Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series, AAAI’20. [paper] [official code]
• Relation Inference among Sensor Time Series in Smart Buildings with Metric Learning, AAAI’20. [paper]
• TapNet: Multivariate Time Series Classification with Attentional Prototype Network, AAAI’20. [paper] [official code]
• RobustSTL: A Robust Seasonal-Trend Decomposition Procedure for Long Time Series, AAAI’19. [paper] [code]
• Estimating the Causal Effect from Partially Observed Time Series, AAAI’19. [paper]
• Adversarial Unsupervised Representation Learning for Activity Time-Series, AAAI’19. [paper]
• Fourier Feature Approximations for Periodic Kernels in Time-Series Modelling, AAAI’18. [paper]

• Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values, AAAI’20. [paper]
• Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting, AAAI’20. [paper] [official code]
• Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting, AAAI’20. [paper] [official code]
• Self-Attention ConvLSTM for Spatiotemporal Prediction, AAAI’20. [paper]
• Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting, AAAI’20. [paper]
• Spatio-Temporal Graph Structure Learning for Traffic Forecasting, AAAI’20. [paper]
• GMAN: A Graph Multi-Attention Network for Traffic Prediction, AAAI’20. [paper] [official code]
• Cogra: Concept-drift-aware Stochastic Gradient Descent for Time-series Forecasting, AAAI’19. [paper]
• Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting, AAAI’19. [paper]
• Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting, AAAI’19. [paper] [official code]
• MRes-RGNN: A Novel Deep Learning based Framework for Traffic Prediction, AAAI’19. [paper]
• DeepSTN+: Context-aware Spatial Temporal Neural Network for Crowd Flow Prediction in Metropolis, AAAI’19. [paper] [official code]
• Incomplete Label Multi-task Deep Learning for Spatio-temporal Event Subtype Forecasting, AAAI’19. [paper]
• Learning Heterogeneous Spatial-Temporal Representation for Bike-sharing Demand Prediction, AAAI’19. [paper]
• Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting, AAAI’19. [paper]

• A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data, AAAI’19. [paper]
• Non-parametric Outliers Detection in Multiple Time Series A Case Study: Power Grid Data Analysis, AAAI’18. [paper]

• RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering, IJCAI’19. [paper]
• E2GAN: End-to-End Generative Adversarial Network for Multivariate Time Series Imputation, IJCAI’19. [paper]
• Causal Inference in Time Series via Supervised Learning, IJCAI’18. [paper]

• PewLSTM: Periodic LSTM with Weather-Aware Gating Mechanism for Parking Behavior Prediction, IJCAI’20. [paper] [official code]
• LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks, IJCAI’20. [paper]
• Cross-Interaction Hierarchical Attention Networks for Urban Anomaly Prediction, IJCAI’20. [paper]
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting, IJCAI’19. [paper]
• Explainable Deep Neural Networks for Multivariate Time Series Predictions, IJCAI’19. [paper]
• Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns. [paper]
• Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. [paper] [official code]
• LC-RNN: A Deep Learning Model for Traffic Speed Prediction. [paper]
• GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction, IJCAI’18. [paper] [official code]
• Hierarchical Electricity Time Series Forecasting for Integrating Consumption Patterns Analysis and Aggregation Consistency, IJCAI’18. [paper]
• NeuCast: Seasonal Neural Forecast of Power Grid Time Series, IJCAI’18. [paper] [official code]
• A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction, IJCAI’17. [paper] [code]
• Hybrid Neural Networks for Learning the Trend in Time Series, IJCAI’17. [paper]

• BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series, IJCAI’19. [paper] [official code]
• Outlier Detection for Time Series with Recurrent Autoencoder Ensembles, IJCAI’19. [paper] [official code]
• Stochastic Online Anomaly Analysis for Streaming Time Series, IJCAI’17. [paper]

• Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest, IJCAI’19. [paper]
• Similarity Preserving Representation Learning for Time Series Clustering, IJCAI’19. [paper]

• A new attention mechanism to classify multivariate time series, IJCAI’20. [paper]

• Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures, SIGMOD’20. [paper] [official code]
• Database Workload Capacity Planning using Time Series Analysis and Machine Learning, SIGMOD’20. [paper]
• Mind the gap: an experimental evaluation of imputation of missing values techniques in time series, VLDB’20. [paper] [official code]
• Active Model Selection for Positive Unlabeled Time Series Classification, ICDE’20. [paper] [official code]
• ExplainIt! — A declarative root-cause analysis engine for time series data, SIGMOD’19. [paper]
• Cleanits: A Data Cleaning System for Industrial Time Series, VLDB’19. [paper]
• Matrix Profile X: VALMOD – Scalable Discovery of Variable-Length Motifs in Data Series, SIGMOD’18. [paper]
• Effective Temporal Dependence Discovery in Time Series Data, VLDB’18. [paper]

• Series2Graph: graph-based subsequence anomaly detection for time series, VLDB’20. [paper] [official code]
• Neighbor Profile: Bagging Nearest Neighbors for Unsupervised Time Series Mining, ICDE’20. [paper]
• Automated Anomaly Detection in Large Sequences, ICDE’20. [paper] [official code]
• User-driven error detection for time series with events, ICDE’20. [paper]

• STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks, WWW’19. [paper] [official code]
• GP-VAE: Deep probabilistic time series imputation, AISTATS’20. [paper] [official code]
• DYNOTEARS: Structure Learning from Time-Series Data, AISTATS’20. [paper]
• Personalized Imputation on Wearable-Sensory Time Series via Knowledge Transfer, CIKM’20. [paper]
• Order-Preserving Metric Learning for Mining Multivariate Time Series, ICDM’20. [paper]
• Learning Periods from Incomplete Multivariate Time Series, ICDM’20. [paper]
• Foundations of Sequence-to-Sequence Modeling for Time Series, AISTATS’19. [paper]

• Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event Forecasting, WWW’20. [paper]
• HTML: Hierarchical Transformer-based Multi-task Learning for Volatility Prediction, WWW’20. [paper] [official code]
• Traffic Flow Prediction via Spatial Temporal Graph Neural Network, WWW’20. [paper]
• Towards Fine-grained Flow Forecasting: A Graph Attention Approach for Bike Sharing Systems, WWW’20. [paper]
• Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting, WWW’20. [paper]
• Spatiotemporal Hypergraph Convolution Network for Stock Movement Forecasting, ICDM’20. [paper]
• Probabilistic Forecasting with Spline Quantile Function RNNs, AISTATS’19. [paper]
• DSANet: Dual self-attention network for multivariate time series forecasting, CIKM’19. [paper]
• RESTFul: Resolution-Aware Forecasting of Behavioral Time Series Data, CIKM’18. [paper]
• Forecasting Wavelet Transformed Time Series with Attentive Neural Networks, ICDM’18. [paper]
• A Flexible Forecasting Framework for Hierarchical Time Series with Seasonal Patterns: A Case Study of Web Traffic, SIGIR’18. [paper]
• Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks, SIGIR’18. [paper] [official code]

• Multivariate Time-series Anomaly Detection via Graph Attention Network, ICDM’20. [paper] [code]
• MERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives, ICDM’20. [paper] [official code]
• Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications, WWW’18. [paper] [official code]