AAAI 2024 accepted papers on Time series
46 of the 2,864 papers accepted at AAAI 2024 (1.6%) are on Time series, compared with 1.8% at AAAI 2023. They fall into 4 topics.
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Topics and papers
Foundation models for time series · 16 papers
- TimesURL: Self-Supervised Contrastive Learning for Universal Time Series Representation Learning (107 citations)
- GPT4MTS: Prompt-based Large Language Model for Multimodal Time-series Forecasting (97 citations)
- CI-STHPAN: Pre-trained Attention Network for Stock Selection with Channel-Independent Spatio-Temporal Hypergraph (41 citations)
- Diffusion Language-Shapelets for Semi-supervised Time-Series Classification (31 citations)
- Graph-Aware Contrasting for Multivariate Time-Series Classification (25 citations)
- Cross-Domain Contrastive Learning for Time Series Clustering (13 citations)
- SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation (12 citations)
- Audio Scanning Network: Bridging Time and Frequency Domains for Audio Classification (10 citations)
- Pharmacokinetics-Informed Neural Network for Predicting Opioid Administration Moments with Wearable Sensors (7 citations)
- Self-Supervised Framework Based on Subject-Wise Clustering for Human Subject Time Series Data (7 citations)
- Energy Efficient Streaming Time Series Classification with Attentive Power Iteration (5 citations)
- Hidden Follower Detection: How Is the Gaze-Spacing Pattern Embodied in Frequency Domain? (5 citations)
and 4 more in the interactive map.
Long-term forecasting · 15 papers
- MSGNet: Learning Multi-Scale Inter-series Correlations for Multivariate Time Series Forecasting (234 citations)
- Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data (114 citations)
- MASTER: Market-Guided Stock Transformer for Stock Price Forecasting (87 citations)
- HDMixer: Hierarchical Dependency with Extendable Patch for Multivariate Time Series Forecasting (72 citations)
- StockMixer: A Simple Yet Strong MLP-Based Architecture for Stock Price Forecasting (49 citations)
- U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting (39 citations)
- Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting (18 citations)
- Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting (10 citations)
- AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data (4 citations)
- Multivariate Time-Series Imagification with Time Embedding in Constrained Environments (Student Abstract) (3 citations)
- Temporal Dependencies and Spatio-Temporal Patterns of Time Series Models (3 citations)
- Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence (3 citations)
and 3 more in the interactive map.
Causal and generative time series · 13 papers
- Latent Diffusion Transformer for Probabilistic Time Series Forecasting (45 citations)
- GraFITi: Graphs for Forecasting Irregularly Sampled Time Series (27 citations)
- Conformal Prediction Regions for Time Series Using Linear Complementarity Programming (20 citations)
- Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context (11 citations)
- IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers (10 citations)
- CityPulse: Fine-Grained Assessment of Urban Change with Street View Time Series (8 citations)
- Cumulative Difference Learning VAE for Time-Series with Temporally Correlated Inflow-Outflow (8 citations)
- Early Detection of Extreme Storm Tide Events Using Multimodal Data Processing (5 citations)
- Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise (5 citations)
- PAC-Bayes Generalisation Bounds for Dynamical Systems including Stable RNNs (4 citations)
- Sequential Modeling of Complex Marine Navigation: Case Study on a Passenger Vessel (Student Abstract) (3 citations)
- Validation, Robustness, and Accuracy of Perturbation-Based Sensitivity Analysis Methods for Time-Series Deep Learning Models (3 citations)
and 1 more in the interactive map.