A list of up-to-date time-series papers in AI venues, tracking the following conferences: WSDM, AAAI, ICLR, AISTATS, SDM, WWW, IJCAI, ICML, KDD, UAI, NeurIPS, CIKM, ICDM, ICASSP
AutoForecast: Automatic Time-Series Forecasting Model Selection
Deep Extreme Mixture Model for Time Series Forecasting
MARINA: An MLP-Attention Model for Multivariate Time-Series Analysis
Stop&Hop: Early Classification of Irregular Time Series
TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Freq Analysis
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities
Residual Correction in Real-Time Traffic Forecasting
Bridging Self-Attention and Time Series Decomposition for Periodic Forecasting
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
Causal Disentanglement for Time Series
BILCO: An Efficient Algorithm for Joint Alignment of Time Series
Non-stationary Transformers: Rethinking the Stationarity in Time Series Forecasting
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks
Generative Time Series Forecasting with Diffusion, Denoise and Disentanglement
Efficient learning of nonlinear prediction models with time-series privileged information
Time Dimension Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting
WaveBound: Dynamically Bounding Error for Stable Time Series Forecasting
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
Dynamic Sparse Network for Time Series Classification: Learning What to “See”
Learning Latent Seasonal-Trend Representations for Time Series Forecasting
Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting
Meta-Learning Dynamics Forecasting Using Task Inference
AutoST: Towards the Universal Modeling of Spatio-temporal Sequences
Predictive Whittle Networks for Time Series
Causal Discovery of Extended Summary Graphs in Time Series
Physics Guided Neural Networks for Spatio-temporal Super-resolution of Turbulent Flows
Causal Forecasting: Generalization Bounds for Autoregressive Models
Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting
Task-Aware Reconstruction for Time-Series Transformer
Multi-Variate Time Series Forecasting on Variable Subsets
ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences
MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting
Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning
Beyond Point Prediction: Capturing Zero-Inflated & Heavy-Tailed Spatiotemporal Data with Deep Extreme Mixture Models
Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams
Local Evaluation of Time Series Anomaly Detection Algorithms
Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting
Learning Differential Operators for Interpretable Time Series Modeling
Non-stationary Time-aware Kernelized Attention for Temporal Event Prediction
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams
Robust Event Forecasting with Spatiotemporal Confounder Learning
Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer
Spatio-Temporal Trajectory Similarity Learning in Road Networks
Selective Cross-city Transfer Learning for Traffic Prediction via Source City Region Re-weighting
MetaPTP: An Adaptive Meta-optimized Model for Personalized Spatial Trajectory Prediction
Human mobility prediction with causal and spatial-constrained multi-task network
Closed-Form Diffeomorphic Transformations for Time Series Alignment
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection
Learning of Cluster-based Feature Importance for Electronic Health Record Time-series
Modeling Irregular Time Series with Continuous Recurrent Units
Domain Adaptation for Time Series Forecasting via Attention Sharing
Utilizing Expert Features for Contrastive Learning of Time-Series Representations
Reconstructing nonlinear dynamical systems from multi-modal time series
Adaptive Conformal Predictions for Time Series
TACTiS: Transformer-Attentional Copulas for Time Series
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion
DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting
The Transfo-k-mer: protein fitness prediction with auto-regressive transformers and inference-time retrieval
Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting
GRELEN: Multivariate Time Series Anomaly Detection from the Perspective of Graph Relational Learning
T-SMOTE: Temporal-oriented Synthetic Minority Oversampling Technique for Imbalanced Time Series Classification
Neural Contextual Anomaly Detection for Time Series
Memory Augmented State Space Model for Time Series Forecasting
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data
A Reinforcement Learning-Informed Pattern Mining Framework for Multivariate Time Series Classification
Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting
Long-term Spatio-Temporal Forecasting via Dynamic Multiple-Graph Attention
When Transfer Learning Meets Cross-City Urban Flow Prediction: Spatio-Temporal Adaptation Matters
Physics-Informed Long-Sequence Forecasting From Multi-Resolution Spatiotemporal Data
FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting
Knowledge Enhanced GAN for IoT Traffic Generation
Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting
Error-Bounded Approximate Time Series Joins Using Compact Dictionary Representations of Time Series
Learning Time-Series Shapelets Enhancing Discriminability
Towards Similarity-Aware Time-Series Classification
Joint Time Series Chain: Detecting Unusual Evolving Trend Across Time Series
Ib-Gan: A Unified Approach for Multivariate Time Series Classification under Class Imbalance
Collaborative Attention Mechanism for Multi-Modal Time Series Classification
Leveraging Dependencies among Learned Temporal Subsequences
Measuring Disentangled Generative Spatio-Temporal Representation
Attentional Gated Res2Net for Multivariate Time Series Classification
Bayesian Continual Imputation and Prediction for Irregularly Sampled Time Series Data
CDX-Net: Cross-Domain Multi-Feature Fusion Modeling via Deep Neural Networks for Multivariate Time Series Forecasting in AIOps
Convex Clustering for Autocorrelated Time Series
Graph Learning from Multivariate Dependent Time Series via a Multi-Attribute Formulation
Multiple Temporal Context Embedding Networks for Unsupervised Time Series Anomaly Detection
On Mini-Batch Training with Varying Length Time Series
Sparse-Group Log-Sum Penalized Graphical Model Learning For Time Series
STGAT-MAD : Spatial-Temporal Graph Attention Network for Multivariate Time Series Anomaly Detection
Robust Probabilistic Time Series Forecasting
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection
LIMESegment: Meaningful, Realistic Time Series Explanations
Using time-series privileged information for provably efficient learning of prediction models
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting
Decoupling Local and Global Representations of Time Series
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation
Increasing the accuracy and resolution of precipitation forecasts using deep generative models
Multivariate Quantile Function Forecaster
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series
Coherence-based Label Propagation over Time Series for Accelerated Active Learning
Huber Additive Models for Non-stationary Time Series Analysis
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting
Guided Network for Irregularly Sampled Multivariate Time Series
Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis
Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification
Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting
On the benefits of maximum likelihood estimation for Regression and Forecasting
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future
UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning
Towards a Rigorous Evaluation of Time-Series Anomaly Detection
Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis
CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting
Reinforcement Learning based Dynamic Model Combination for Time Series Forecasting
TS2Vec: Towards Universal Representation of Time Series
I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding
Clustering Interval-Censored Time-Series for Disease Phenotyping
Conditional Loss and Deep Euler Scheme for Time Series Generation
Conditional Local Convolution for Spatio-Temporal Meteorological Forecasting
Learning and Dynamical Models for Sub-Seasonal Climate Forecasting: Comparison and Collaboration
Graph Neural Controlled Differential Equations for Traffic Forecasting
Dynamic Manifold Learning for Land Deformation Forecasting
HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting
PrEF: Probabilistic Electricity Forecasting via Copula-Augmented State Space Model
LIMREF: Local Interpretable Model Agnostic Rule-Based Explanations for Forecasting, with an Application to Electricity Smart Meter Data
NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-Task Financial Forecasting
CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting
AGNN-RNNApproach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction
SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss
Feature Importance Explanations for Temporal Black-Box Models
MuMu:Cooperative Multitask Learning-based Guided Multimodal Fusion
ESC-GAN: Extending Spatial Coverage of Physical Sensors
ST-GSP: Spatial-Temporal Global Semantic Representation Learning for Urban Flow Prediction
Translating Human Mobility Forecasting through Natural Language Generation
A New Class of Polynomial Activation Functions of Deep Learning for Precipitation Forecasting
CMT-Net: A Mutual Transition Aware Framework for Taxicab Pick-ups and Drop-offs Co-Prediction
Predicting Human Mobility via Graph Convolutional Dual-attentive Networks
RLMob: Deep Reinforcement Learning for Successive Mobility Prediction
Towards Interpretability and Personalization: A Predictive Framework for Clinical Time-series Analysis
Towards Generating Real-World Time Series Data
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions
CASPITA: Mining Statistically Significant Paths in Time Series Data from an Unknown Network
Multi-way Time Series Join on Multi-length Patterns
Ultra fast warping window optimization for Dynamic Time Warping
Disentangled Deep Multivariate Hawkes Process for Learning Event Sequences
Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting
SSDNet: State Space Decomposition Neural Network for Time Series Forecasting
Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting
DAC-ML: Domain Adaptable Continuous Meta-Learning for Urban Dynamics Prediction
Sequential Diagnosis Prediction with Transformer and Ontological Representation
Partial Differential Equation Driven Dynamic Graph Networks for Predicting Stream Water Temperature
Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Records
SCEHR: Supervised Contrastive Learning for Clinical Risk Predictions with Electronic Health Records
LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values
MERITS: Medication Recommendation for Chronic Disease with Irregular Time-Series
PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series
Matrix Profile XXIII: Contrast Profile: A Novel Time Series Primitive that Allows Real World Classification
LOGIC: Probabilistic Machine Learning for Time Series Classification
SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series
STING: Self-attention based Time-series Imputation Networks using GAN
Spikelet: An Adaptive Symbolic Approximation for Finding Higher-Level Structure in Time Series
Streaming Dynamic Graph Neural Networks for Continuous-Time Temporal Graph Modeling
TCube: Domain-Agnostic Neural Time-series Narration
TEST-GCN: Topologically Enhanced Spatial-Temporal Graph Convolutional Networks for Traffic Forecasting
ClaSP - Time Series Segmentation
Learning to Learn the Future: Modeling Concept Drifts in Time Series Prediction
AdaRNN: Adaptive Learning and Forecasting of Time Series
Learning Saliency Maps to Explain Deep Time Series Classifiers
Actionable Insights in Urban Multivariate Time-series
Integrating Static and Time-Series Data in Deep Recurrent Models for Oncology Early Warning Systems
Hierarchical Semantics Matching For Heterogeneous Spatio-temporal Sources
HASTE: A Distributed System for Hybrid and Adaptive Processing on Streaming Spatial-Textual Data
Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction
Spatio-Temporal-Social Multi-Feature-based Fine-Grained Hot Spots Prediction for Content Delivery Services in 5G Era
Into the Unobservables: A Multi-range Encoder-decoder Framework for COVID-19 Prediction
What is Next when Sequential Prediction Meets Implicitly Hard Interaction?
Stock Trend Prediction with Multi-granularity Data: A Contrastive Learning Approach with Adaptive Fusion
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
Dynamical Wasserstein Barycenters for Time-series Modeling
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
Conformal Time-series Forecasting
Coresets for Time Series Clustering
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data
Adjusting for Autocorrelated Errors in Neural Networks for Time Series
Deep Explicit Duration Switching Models for Time Series
Online false discovery rate control for anomaly detection in time series
Topological Attention for Time Series Forecasting
Time-series Generation by Contrastive Imitation
Probabilistic Transformer For Time Series Analysis
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data
PLSO: A generative framework for decomposing nonstationary time-series into piecewise stationary oscillatory components
Subseasonal Climate Prediction in the Western US using Bayesian Spatial Models
MiniRocket: A Fast (Almost) Deterministic Transform for Time Series Classification
Deep Learning Embeddings for Data Series Similarity Search
Fast and Accurate Partial Fourier Transform for Time Series Data
Representation Learning of Multivariate Time Series using a Transformer Framework
ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting
Statistical models coupling allows for complex localmultivariate time series analysis
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting
Quantifying Uncertainty in Deep Spatiotemporal Forecasting
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
Weakly Supervised Spatial Deep Learning based on Imperfect Training Labels with Location Errors
A PLAN for Tackling the Locust Crisis in East Africa: Harnessing Spatiotemporal Deep Models for Locust Movement Forecasting
Graph Deep Factor Model for Cloud Utilization Forecasting
JOHAN: A Joint Online Hurricane Trajectory and Intensity Forecasting Framework
Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts
Attentive Heterogeneous Graph Embedding for Job Mobility Prediction
TrajNet: A Trajectory-Based Deep Learning Model for Traffic Prediction
End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series
Neural Rough Differential Equations for Long Time Series
Voice2Series: Reprogramming Acoustic Models for Time Series Classification
Whittle Networks: A Deep Likelihood Model for Time Series
Necessary and sufficient conditions for causal feature selection in time series with latent common causes
Explaining Time Series Predictions with Dynamic Masks
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
Conformal prediction interval for dynamic time-series
Approximation Theory of Convolutional Architectures for Time Series Modelling
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
Variance Reduced Training with Stratified Sampling for Forecasting Models
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
A Structured Observation Distribution for Generative Biological Sequence Prediction and Forecasting
Principled Simplicial Neural Networks for Trajectory Prediction
TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data
Time-Aware Multi-Scale RNNs for Time Series Modeling
Time-Series Representation Learning via Temporal and Contextual Contrasting
Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation
Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting
Multi-series Time-aware Sequence Partitioning for Disease Progression Modeling
Multi-version Tensor Completion for Time-delayed Spatio-temporal Data
Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning
Hierarchical Adaptive Temporal-Relational Modeling for Stock Trend Prediction
Residential Electric Load Forecasting via Attentive Transfer of Graph Neural Networks
TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning
Multimodal Transformer Networks for Pedestrian Trajectory Prediction
Cooperative Joint Attentive Network for Patient Outcome Prediction on Irregular Multi-Rate Multivariate Health Data
DeepFEC: Energy Consumption Prediction under Real-World Driving Conditions for Smart Cities
HINTS: Citation Time Series Prediction for New Publications viaDynamic Heterogeneous Information Network Embedding
Network of Tensor Time Series
Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series
REST: Reciprocal Framework for Spatiotemporal coupled predictions
SDFVAE: Static and Dynamic Factorized VAE for Anomaly Detection of Multivariate CDN KPIs
SRVAR: Joint Discrete Hidden State Discovery and Structure Learning from Time Series Data
Time-series Change Point Detection with Self-Supervised Contrastive Predictive Coding
STUaNet: Understanding uncertainty in spatiotemporal collective human mobility
Learning Time-series Shapelets via Supervised Feature Selection
Robust Dual Recurrent Neural Networks for Financial Time Series Prediction
Attention-Based Autoregression for Accurate and Efficient Multivariate Time Series Forecasting
UNIANO: robust and efficient anomaly consensus in time series sensitive to cross-correlated anomaly profiles
Inter-Series Attention Model for COVID-19 Forecasting
Stochastic Time Series Representation for Interval Pattern Mining via Gaussian Processes
Hypa: Efficient Detection of Path Anomalies in Time Series Data on Networks
Filling Missing Values on Wearable-Sensory Time Series Data
Lag-Aware Multivariate Time-Series Segmentation
Learning Time-series Shapelets for Optimizing Partial AUC
A Fine-grained Graph-based Spatiotemporal Network for Bike Flow Prediction in Bike-sharing Systems
Semantic Discord: Finding Unusual Local Patterns for Time Series
MT-STNets: Multi-Task Spatial-Temporal Networks for Multi-Scale Traffic Prediction
GDTW: A Novel Differentiable DTW Loss for Time Series Tasks
Recursive Input and State Estimation: A General Framework for Learning from Time Series with Missing Data
Semi-supervised Time Series Classification by Temporal Relation Prediction
Spatiotemporal Attention for Multivariate Time Series Prediction and Interpretation
Tabular Transformers for Modeling Multivariate Time Series
Two-Stage Framework for Seasonal Time Series Forecasting
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series
Aligning Time Series on Incomparable Spaces
Differentiable Divergences Between Time Series
Multi-Time Attention Networks for Irregularly Sampled Time Series
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
Generative Time-series Modeling with Fourier Flows
Discrete Graph Structure Learning for Forecasting Multiple Time Series
Clairvoyance: A Pipeline Toolkit for Medical Time Series
HalentNet: Multimodal Trajectory Forecasting with Hallucinative Intents
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
Trajectory Prediction using Equivariant Continuous Convolution
Meta-Learning Framework with Applications to Zero-Shot Time-Series Forecasting
Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting
Dynamic Gaussian Mixture Based Deep Generative Model for Robust Forecasting on Sparse Multivariate Time Series
Second Order Techniques for Learning Time-Series with Structural Breaks
Correlative Channel-Aware Fusion for Multi-View Time Series Classification
Learnable Dynamic Temporal Pooling for Time Series Classification
Time Series Domain Adaptation via Sparse Associative Structure Alignment
Learning Representations for Incomplete Time Series Clustering
Temporal Latent Autoencoder: A Method for Probabilistic Multivariate Time Series Forecasting
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification
Time Series Anomaly Detection with Multiresolution Ensemble Decoding
Joint-Label Learning by Dual Augmentation for Time Series Classification
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Generative Semi-Supervised Learning for Multivariate Time Series Imputation
Outlier Impact Characterization for Time Series Data
Robust Spatio-Temporal Purchase Prediction via Deep Meta Learning
Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances
A Multi-Step-Ahead Markov Conditional Forward Model with Cube Perturbations for Extreme Weather Forecasting
Time-Series Event Prediction with Evolutionary State Graph
Modeling Inter-station Relationships with Attentive Temporal Graph Convolutional Network for Air Quality Prediction
Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Time Intervals
FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection
Predicting Crowd Flows via Pyramid Dilated Deeper Spatial-temporal Network