NeurIPS 2024 Past Time series
NeurIPS Workshop on Time Series in the Age of Large Models
NeurIPS 2024 TSALM Workshop
- Submission deadline
- Sep 16, 2024, 11:59 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (69)
Fetched from OpenReview (v2) on 2026-06-10.
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♠ SPADE ♠ Split Peak Attention DEcomposition
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A Language Model-Guided Framework for Mining Time Series with Distributional Shifts
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Adaptive Information Routing for Multi Modal Time Series Forecasting
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Align and Fine-Tune: Enhancing LLMs for Time-Series Forecasting
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Benchmarking out-of-the-box forecasters of varying scales in biology
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Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models
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Catching the Spikes: Heteroscedastic Uncertainty Quantification for Enhanced Malaria Prediction
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Context is Key: A Benchmark for Forecasting with Essential Textual Information
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Critical Evaluation of Time Series Foundation Models in Demand Forecasting
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Deep Temporal Deaggregation: Large-Scale Spatio-Temporal Generative Models
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Do we really need Foundation Models for multi-step-ahead Epidemic Forecasting?
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Domain-adapted Lag-Llama for Time Series Forecasting in the African Retail Sector.
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Effectively Leveraging Exogenous Information across Neural Forecasters
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Efficient Time Series Processing for Transformers and State-Space Models through Token Merging
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Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling
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Enhance Time Series Modeling by Integrating LLM
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Enhancing Multivariate Time Series Forecasting via Multi-Task Learning and Random Matrix Theory
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Fine-Tuning a Time Series Foundation Model with Wasserstein Loss
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From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption
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General-Purpose Brain Foundation Models for Time-Series Neuroimaging Data
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Generalized Prompt Tuning: How to Use a Frozen Pre-Trained Univariate Time Series Foundation Model for Multivariate Time Series Prediction
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GIFT-Eval: A Benchmark for General Time Series Forecasting Model Evaluation
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Hierarchical Time Series Forecasting Via Latent Mean Encoding
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Implicit Reasoning in Deep Time Series Forecasting
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In-context Quantile Regression for Multi-product Inventory Management using Time-series Transformers
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Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Series
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Joint Embedding go Temporal
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KAN4Drift: Are KAN Effective for Identifying and Tracking Concept Drift in Time Series?
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LETS-C: Leveraging Text Embedding for Time Series Classification
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Leveraging Periodicity for Robustness with Multi-modal Mood Pattern Models
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LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration
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LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data
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Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models
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Masking the Gaps: An Imputation-Free Approach to Time Series Modeling with Missing Data
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Maven: A Multimodal Foundation Model for Supernova Science
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Measuring Pre-training Data Quality without Labels for Time Series Foundation Models
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MEDS-torch: An ML Pipeline for Inductive Experiments for EHR Medical Foundation Models
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Mixture of Experts for Time Series Foundation Models
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Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach
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PaPaGei: Open Foundation Models for Optical Physiological Signals
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Partial Channel Dependence with Channel Masks for Time Series Foundation Model
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Preventing Conflicting Gradients in Neural Temporal Point Process Models for Irregular Time Series Data
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PRIMUS: Pretraining IMU Encoders with Multimodal Self-Supervision
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Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models the Answer?
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Reimagining Time Series Foundation Models: Metadata and State-Space Model Perspectives
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Revisiting Masked Auto-Encoders for ECG-Language Representation Learning
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Scaling to Billion Parameters for Time Series Foundation Models with Mixture of Experts
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Scaling-laws for Large Time-series Models
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Sequential Order-Robust Mamba for Time Series Forecasting
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Stochastic Sparse Sampling: A Framework for Local Explainability in Variable-Length Medical Time Series
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Test-Time Learning For Time Series Forecasting
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Text2Freq: Learning Series Patterns from Text via Frequency Domain
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The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features
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Time Series under Temporal Label Noise
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TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data
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TimeSeriesExam: A Time Series Understanding Exam
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Towards Large-scale Clinical Multi-variate Time-series Datasets
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Towards Long-Context Time Series Foundation Models
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Towards Resolution-Aware Retrieval Augmented Zero-Shot Forecasting
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Towards Time-Series Reasoning with LLMs
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Towards Unbiased Evaluation of Time-series Anomaly Detector
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TrajGPT: Healthcare Time-Series Representation Learning for Trajectory Prediction
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Transformer-based Time-Series Biomarker Discovery for COPD Diagnosis
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UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting
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Unveiling and Manipulating Concepts in Time Series Foundation Models
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Unveiling the Potential of Text in High-Dimensional Time Series Forecasting
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Weakly-supervised Multi-sensor Anomaly Detection with Time-series Foundation Models
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When Larger Isn’t Better: Lightweight CNNs Outperform Large Time-Series Models in Classification of Oil and Gas Drilling Data
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Zero shot time series forecasting using Kolgomorov Arnold Networks