A list of up-to-date time-series papers in AI venues, tracking the following conferences: WSDM, AAAI, ICLR, AISTATS, ICASSP, SDM, WWW, IJCAI, ICML, KDD, NeurIPS, CIKM, ICDM
Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series
Transformers with Loss Shaping Constraints for Long-Term Time Series Forecasting
CauDiTS: Causal Disentangled Domain Adaptation of Multivariate Time Series
A Vector Quantization Pretraining Method for EEG Time Series with Random Projection and Phase Alignment
SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters
Learning Optimal Projection for Forecast Reconciliation of Hierarchical Time Series
Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
Bayesian Online Multivariate Time Series Imputation with Functional Decomposition
CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables
Position Paper: What Can Large Language Models Tell Us about Time Series Analysis
Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization
UP2ME: Univariate Pre-training to Multivariate Fine-tuning as a General-purpose Framework for Multivariate Time Series Analysis
Position Paper: Quo Vadis, Unsupervised Time Series Anomaly Detection?
Discovering Mixtures of Structural Causal Models from Time Series Data
Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
An Analysis of Linear Time Series Forecasting Models
TimeMIL: Advancing Multivariate Time Series Classification via a Time-aware Multiple Instance Learning
TSLANet: Rethinking Transformers for Time Series Representation Learning
An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series
Timer: Transformers for Time Series at Scale
Time Weaver: A Conditional Time Series Generation Model
SIN: Selective and Interpretable Normalization for Long-Term Time Series Forecasting
Unified Training of Universal Time Series Forecasting Transformers
Time Series Diffusion in the Frequency Domain
Reservoir Computing for Short High-Dimensional Time Series: an Application to SARS-CoV-2 Hospitalization Forecast
Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach
Learning Causal Relations from Subsampled Time Series with Two Time-Slices
$S^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
Probabilistic time series modeling with decomposable denoising diffusion model
MOMENT: A Family of Open Time-series Foundation Models
TimeX++: Learning Time-Series Explanations with Information Bottleneck
A decoder-only foundation model for time-series forecasting
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
Conformal prediction for multi-dimensional time-series
TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling
Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
Large Language Models for Time Series: A Survey
Sub-Adjacent Transformer: Improving Time Series Anomaly Detection with Reconstruction Error from Sub-Adjacent Neighborhoods
An NCDE-based Framework for Universal Representation Learning of Time Series
Self-adaptive Extreme Penalized Loss for Imbalanced Time Series Prediction
SDformer: Transformer with Spectral Filter and Dynamic Attention for Multivariate Time Series Long-term Forecasting
Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation
SCAT: A Time Series Forecasting with Spectral Central Alternating Transformers
Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection
SaSDim:Self-Adaptive Noise Scaling Diffusion Model for Spatial Time Series Imputation
Decoupled Invariant Attention Network for Multivariate Time-series Forecasting
Denoising-Aware Contrastive Learning for Noisy Time Series
Contrastive Learning Is Not Optimal for Quasiperiodic Time Series
Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting
Skip-Timeformer: Skip-Time Interaction Transformer for Long Sequence Time-Series Forecasting
Temporal Graph ODEs for Irregularly-Sampled Time Series
LeRet: Language-Empowered Retentive Network for Time Series Forecasting
Reconstructing Missing Variables for Multivariate Time Series Forecasting via Conditional Generative Flows
SpecAR-Net: Spectrogram Analysis and Representation Network for Time Series
Perturbation Guiding Contrastive Representation Learning for Time Series Anomaly Detection
Disentangling Domain and General Representations for Time Series Classification
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting
Dynamic Multi-Network Mining of Tensor Time Series
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection
Breaking the Time-Frequency Granularity Discrepancy in Time-Series Anomaly Detection
E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series
Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective
Pupae: Intuitive and Actionable Explanations for Time Series Anomalies
Pattern-Based Time Series Semantic Segmentation with Gradual State Transitions
Towards Entity-Aware Conditional Variational Inference for Heterogeneous Time-Series Prediction: An Application to Hydrology
EBV: Electronic Bee-Veterinarian for Principled Mining and Forecasting of Honeybee Time Series
Time-Transformer: Integrating Local and Global Features for Better Time Series Generation
Message Propagation Through Time: An Algorithm for Sequence Dependency Retention in Time Series Modeling
Analysis of Causal and Non-Causal Convolution Networks for Time Series Classification
Better Batch for Deep Probabilistic Time Series Forecasting
Fitting ARMA Time Series Models without Identification: A Proximal Approach
Multivariate Time Series Forecasting By Graph Attention Networks With Theoretical Guarantees
An Online Bootstrap for Time Series
Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks
Mixture-of-Linear-Experts for Long-term Time Series Forecasting
Unsupervised Change Point Detection in Multivariate Time Series
Multi-resolution Time-Series Transformer for Long-term Forecasting
Random Oscillators Network for Time Series Processing
Latent Trajectory Learning for Limited Timestamps under Distribution Shift over Time
ClimODE: Climate Forecasting With Physics-informed Neural ODEs
Soft Contrastive Learning for Time Series
FITS: Modeling Time Series with 10k Parameters
ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
Inherently Interpretable Time Series Classification via Multiple Instance Learning
Generative Learning for Financial Time Series with Irregular and Scale-Invariant Patterns
Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series
Towards Transparent Time Series Forecasting
Multi-Resolution Diffusion Models for Time Series Forecasting
Diffusion-TS: Interpretable Diffusion for General Time Series Generation
Explaining Time Series via Contrastive and Locally Sparse Perturbations
Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting
Copula Conformal prediction for multi-step time series prediction
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
Interpretable Sparse System Identification: Beyond Recent Deep Learning Techniques on Time-Series Prediction
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
Learning to Embed Time Series Patches Independently
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series
T-Rep: Representation Learning for Time Series using Time-Embeddings
Parametric Augmentation for Time Series Contrastive Learning
Conditional Information Bottleneck Approach for Time Series Imputation
TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting
VQ-TR: Vector Quantized Attention for Time Series Forecasting
Disentangling Time Series Representations via Contrastive based l-Variational Inference
Retrieval-Based Reconstruction For Time-series Contrastive Learning
RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies
Transformer-Modulated Diffusion Models for Probabilistic Multivariate Time Series Forecasting
Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach
Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators
STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction
Periodicity Decoupling Framework for Long-term Series Forecasting
Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values
DAM: A Foundation Model for Forecasting
Leveraging Generative Models for Unsupervised Alignment of Neural Time Series Data
ARM: Refining Multivariate Forecasting with Adaptive Temporal-Contextual Learning
Language Models Represent Space and Time
Self-Supervised Contrastive Forecasting
HDMixer: Hierarchical Dependency with Extendable Patch for Multivariate Time Series Forecasting
Latent Diffusion Transformer for Probabilistic Time Series Forecasting
Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting
Diffusion Language-Shapelets for Semi-supervised Time-Series Classification
U-mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting
Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting
Cross-Domain Contrastive Learning for Time Series Clustering
When Model Meets New Normals: Test-Time Adaptation for Unsupervised Time-Series Anomaly Detection
GraFITi: Graphs for Forecasting Irregularly Sampled Time Series
MSGNet: Learning Multi-Scale Inter-series Correlations for Multivariate Time Series Forecasting
Graph-Aware Contrasting for Multivariate Time-Series Classification
Energy-Efficient Streaming Time Series Classification with Attentive Power Iteration
Cumulative Difference Learning VAE for Time-Series with Temporally Correlated Inflow-Outflow
IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers
CUTS+: High-Dimensional Causal Discovery from Irregular Time-Series
Efficient Representation Learning of Satellite Image Time Series and Their Fusion for Spatiotemporal Applications
TimesURL: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning
SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation
CGS-Mask: Making Time Series Predictions Intuitive for All
Cross-modal Self-Supervised Learning for Time-series through Latent Masking
NeuralReconciler for Hierarchical Time Series Forecasting
Continuous-time Autoencoders for Regular and Irregular Time Series Imputation
CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting
CityCAN: Causal Attention Network for Citywide Spatio-Temporal Forecasting
MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization
PatternRCA: A Pattern-aware Root Cause Analysis Framework For Multi-Dimensional Time Series
MTT-DynGL: Towards Multidimensional Topology-Oriented Time-Series Dynamic Graphs Learning Model
Rethinking Temporal Dependencies in Multiple Time Series: A Use Case in Financial Data
Koopman Invertible Autoencoder: Leveraging Forward and Backward Dynamics for Temporal Modeling
Self-supervised Pre-Training for Robust and Generic Spatial-Temporal Representations
Compatible Transformer for Irregularly Sampled Multivariate Time Series
Explainable Adaptive Tree-Based Model Selection for Time-Series Forecasting
Boosting Urban Prediction via Addressing Spatial-Temporal Distribution Shift
GAFNO: Gated Adaptive Fourier Neural Operator for Task-Agnostic Time Series Modeling
A Symbolic Representation of Two-Dimensional Time Series for Arbitrary Length DTW Motif
Counterfactual Explanations for Time Series Forecasting
ST-MoE: Spatio-Temporal Mixture-of-Experts for Debiasing in Traffic Prediction
Spatio-Temporal Meta Contrastive Learning
Spatial-Temporal Graph Boosting Networks: Enhancing Spatial-Temporal Graph Neural Networks via Gradient Boosting
MadSGM: Multivariate Anomaly Detection with Score-based Generative Models
Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data
Adaptive Graph Neural Diffusion for Traffic Demand Forecasting
CARPG: Cross-City Knowledge Transfer for Traffic Accident Prediction via Attentive Region-Level Parameter Generation
GBTTE: Graph Attention Network Based Bus Travel Time Estimation
Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank
Follow the Will of the Market: A Context-Informed Drift-Aware Method for Stock Prediction
Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction
Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction
Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting
Enhancing Spatio-temporal Traffic Prediction through Urban Human Activity Analysis
HST-GT: Heterogeneous Spatial-Temporal Graph Transformer for Delivery Time Estimation in Warehouse-Distribution Integration E-Commerce
Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
MLPST: MLP is All You Need for Spatio-Temporal Prediction
Mask- and Contrast-Enhanced Spatio-Temporal Learning for Urban Flow Prediction
Region Profile Enhanced Urban Spatio-Temporal Prediction via Adaptive Meta-Learning
PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction
GCformer: An Efficient Solution for Accurate and Scalable Long-Term Multivariate Time Series Forecasting
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
Meta-Transfer-Learning for Time Series Data with Extreme Events: An Application to Water Temperature Prediction
Temporal Convolutional Explorer Helps Understand 1D-CNN's Learning Behavior in Time Series Classification from Frequency Domain
DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction
TriD-MAE: A Generic Pre-trained Model for Multivariate Time Series with Missing Values
FAMC-Net: Frequency Domain Parity Correction Attention and Multi-Scale Dilated Convolution for Time Series Forecasting
DuoGAT: Dual Time-oriented Graph Attention Networks for Accurate, Efficient and Explainable Anomaly Detection on Time-series
Khronos: A Real-Time Indexing Framework for Time Series Databases on Large-Scale Performance Monitoring Systems
Density-Aware Temporal Attentive Step-wise Diffusion Model For Medical Time Series Imputation
A Co-training Approach for Noisy Time Series Learning
Time-series Shapelets with Learnable Lengths
Toward a Foundation Model for Time Series Data
Unlocking the Potential of Deep Learning in Peak-Hour Series Forecasting
TemDep: Temporal Dependency Priority for Multivariate Time Series Prediction
Clustering-property Matters: A Cluster-aware Network for Large Scale Multivariate Time Series Forecasting
Learning Visibility Attention Graph Representation for Time Series Forecasting
Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting
Unleashing the Power of Shared Label Structures for Human Activity Recognition
One Fits All: Power General Time Series Analysis by Pretrained LM
MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels
Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective
Causal Discovery from Subsampled Time Series with Proxy Variables
Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting
What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?
CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement
Time Series Kernels based on Nonlinear Vector AutoRegressive Delay Embeddings
WildfireSpreadTS: A dataset of multi-modal time series for wildfire spread prediction
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
Conformal Scorecasting: Anticipatory Uncertainty Quantification for Distribution Shift in Time Series
Drift doesn't Matter: Dynamic Decomposition with Diffusion Reconstruction for Unstable Multivariate Time Series Anomaly Detection
Sparse Deep Learning for Time Series Data: Theory and Applications
Large Language Models Are Zero Shot Time Series Forecasters
Sparse Graph Learning from Spatiotemporal Time Series
BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis
WITRAN: Water-wave Information Transmission and Recurrent Acceleration Network for Long-range Time Series Forecasting
Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction
Causal Discovery in Semi-Stationary Time Series
Conformal Prediction for Time Series with Modern Hopfield Networks
Time Series as Images: Vision Transformer for Irregularly Sampled Time Series
FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning
BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency
FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space
On the Constrained Time-Series Generation Problem
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series
Generative Causal Interpretation Model for Spatio-Temporal Representation Learning
Localised Adaptive Spatial-Temporal Graph Neural Network
Maintaining the status quo-Capturing invariant relations for OOD spatiotemporal learning
Pattern Expansion and Consolidation on Evolving Graphs for Continual Traffic Prediction
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
Precursor-of-Anomaly Detection for Irregular Time Series
Transferable Graph Structure Learning for Graph-Based Traffic Forecasting Across Cities
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting
FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
Warpformer: A Multi-Scale Modeling Approach for Irregular Clinical Time Series
WHEN: A Wavelet-DTW Hybrid Attention Network for Heterogeneous Time Series Analysis
Source-Free Domain Adaptation with Temporal Imputation for Time Series Data
Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders
Sparse Binary Transformers for Multivariate Time Series Modeling
Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models
An Observed Value Consistent Diffusion Model for Imputing Missing Values in Multivariate Time Series
Parameter-free Spikelet: Discovering Different Length and Warped Time Series Motifs using an Adaptive Time Series Representation
DoubleAdapt: A Meta-Learning Approach to Incremental Learning for Stock Trend Forecasting
Web-Based Long-Term Spine Treatment Outcome Forecasting
Domain Adaptation for Time Series Under Feature and Label Shifts
Learning Perturbations to Explain Time Series Predictions
Non-autoregressive Conditional Diffusion Models for Time Series Prediction
Probabilistic Imputation for Time-series Classification with Missing Data
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation
Learning Deep Time-index Models for Time Series Forecasting
Self-Interpretable Time Series Prediction with Counterfactual Explanations
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series
Neural Stochastic Differential Games for Time-series Analysis
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting
Prototype-oriented unsupervised anomaly detection for multivariate time series
Sequential Monte Carlo Learning for Time Series Structure Discovery
Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series
Feature Programming for Multivariate Time Series Prediction
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series
Deep Latent State Space Models for Time-Series Generation
Context Consistency Regularization for Label Sparsity in Time Series
Sequential Predictive Conformal Inference for Time Series
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts
Regression with Sensor Data Containing Incomplete Observations
CTW: Confident Time-Warping for Time-Series Label-Noise Learning
Latent Processes Identification From Multi-View Time Series
SMARTformer: Semi-Autoregressive Transformer with Efficient Integrated Window Attention for Long Time Series Forecasting
DeLELSTM: Decomposition-based Linear Explainable LSTM to Capture Instantaneous and Long-term Effects in Time Series
Learning Gaussian Mixture Representations for Tensor Time Series Forecasting
pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting
Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data
Not Only Pairwise Relationships: Fine-Grained Relational Modeling for Multivariate Time Series Forecasting
Self-Recover: Forecasting Block Maxima in Time Series from Predictors with Disparate Temporal Coverage Using Self-Supervised Learning
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification
TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification
INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging
Attention-Based Multi-modal Missing Value Imputation for Time Series Data with High Missing Rate
Probabilistic Decomposition Transformer for Time Series Forecasting
PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time Series
Context-aware Domain Adaptation for Time Series Anomaly Detection
GIST: Graph Inference for Structured Time Series
Discovering Multi-Dimensional Time Series Anomalies with K of N Anomaly Detection
Time-delayed Multivariate Time Series Predictions
Deep Contrastive One-Class Time Series Anomaly Detection
Exact and Heuristic Approaches to Speeding Up the MSM Time Series Distance Computation
Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics
StAGN: Spatial-Temporal Adaptive Graph Network via Constranstive Learning for Sleep Stage Classification
STM-GAIL: Spatial-Temporal Meta-GAIL for Learning Diverse Human Driving Strategies
Mini-Batch Learning Strategies for modeling long term temporal dependencies: A study in environmental applications
A Two-View EEG Representation for Brain Cognition by Composite Temporal-Spatial Contrastive Learning
Spatiotemporal Classification with limited labels using Constrained Clustering for large datasets
Vector Quantized Time Series Modeling with a Bidirectional Prior Model
Root Cause Identification for Collective Anomalies given a Summary Causal Graph and Time Series
Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression
Coherent Probabilistic Forecasting of Temporal Hierarchies
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting
MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting
Unsupervised Model Selection for Time Series Anomaly Detection
Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting
Dynamical systems embedding with a physics-informed convolutional network
Contrastive Learning for Unsupervised Domain Adaptation of Time Series
Out-of-distribution Representation Learning for Time Series Classification
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting
Multivariate Time-series Imputation with Disentangled Temporal Representations
CUTS: Neural Causal Discovery from Unstructured Time-Series Data
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
Temporal Dependencies in Feature Importance for Time Series Prediction
Effectively Modeling Time Series with Simple Discrete State Spaces
Learning Fast and Slow for Time Series Forecasting
Recursive Time Series Data Augmentation
Koopman Neural Operator Forecaster for Time-series with Temporal Distributional Shifts
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms
Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths
Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction
Causal Conditional Hidden Markov Model for Multimodal Traffic Prediction
Automated Spatio-Temporal Multi-Task Learning
Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling
Scalable Spatiotemporal Graph Neural Networks
Unifying Electricity Forecasting with Robust, Flexible, and Explainable Machine Learning Algorithms
Easy Begun Is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout
Spatio-Temporal Neural Structural Causal Models for Bike Flow Prediction
Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction
WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series
Temporal-Frequency Co-Training for Time Series Semi-Supervised Learning
SEnsor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation
Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose
Causal Recurrent Variational Autoencoder for Medical Time Series Generation
AEC-GAN: Adversarial Error Correction GANs for Auto-Regressive Long Time-series Generation
An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural Networks
Are Transformers Effective for Time Series Forecasting?
Supervised Contrastive Few-shot Learning for High-frequency Time Series
Hierarchical Contrastive Learning for Temporal Point Processes
Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis
SVP-T: A Shape-Level Variable-Position Transformer for Multivariate Time Series Classification
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
WSiP: Wave Superposition Inspired Pooling for Dynamic Interactions-Aware Trajectory Prediction
PEN: Prediction-Explanation Network to Forecast Stock Price Movement with Better Explainability
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
Detecting Multivariate Time Series Anomalies with Zero Known Label
Learning Dynamic Temporal Relations with Continuous Graph for Multivariate Time Series Forecasting
DyCVAE: Learning Dynamic Causal Factors for Non-stationary Series Domain Generalization
InParformer: Evolutionary Decomposition Transformers with Interactive Parallel Attention for Long-Term Time Series Forecasting
SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies
Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders
PrimeNet: Pre-Training for Irregular Multivariate Time Series
Time Series Contrastive Learning with Information-Aware Augmentations
Black-box Adversarial Attack on Time Series Classification
A Multi-graph Fusion Based Spatiotemporal Dynamic Learning Framework
Telecommunication Traffic Forecasting via Multi-task Learning
Adversarial Autoencoder for Unsupervised Time Series Anomaly Detection and Interpretation
Ask “Who”, Not “What”: Bitcoin Volatility Forecasting with Twitter Data
DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms
Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction
Matrix Profile XXV: Introducing Novelets: A Primitive that Allows Online Detection of Emerging Behaviors in Time Series
Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description
Class-Specific Explainability for Deep Time Series Classifiers
Matrix Profile XXVI: Mplots: Scaling Time Series Similarity Matrices to Massive Data
Robust Time Series Chain Discovery with Incremental Nearest Neighbors
Self-explaining Hierarchical Model for Intraoperative Time Series
MRM2: Multi-Relationship Modeling Module for Multivariate Time Series Classification
Temporal Knowledge Graph Reasoning via Time- Distributed Representation Learning
Text-enhanced Multi-Granularity Temporal Graph Learning for Event Prediction
Mest-GAN: Cross-City Urban Traffic Estimation with Meta Spatial-Temporal Generative Adversarial Network
STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19
Spatiotemporal Contextual Consistency Network for Precipitation Nowcasting
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
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
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
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
