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