LLM-Based Time Series Forecasting

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13 papers in the last four weeks, up 117% on the four weeks before. 0.1% of all new papers.

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Deep Learning Network-Temporal Models For Traffic Prediction
Mar 12, 2026Yufeng Xin, Ethan FanMultivariate Time Series Forecasting LLM-Based Time Series Forecasting
RENCI, University of North Carolina at Chapel Hill Chapel Hill, NC, USA
GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting
Jan 8, 2026Shovito Barua Soumma, Hassan GhasemzadehTime Series Forecasting Blood Glucose Forecasting
College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA · School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA
A Fast and Effective Solution to the Problem of Look-ahead Bias in LLMs
Dec 7, 2025Humzah Merchant, Bradford LevyLLM-Based Time Series Forecasting LLM Unlearning
University of Chicago Chicago, IL 60637
Auditable Context-Aware HFMD Forecasting with Structured LLM Agents
Nov 28, 2025Joongwon Chae, Runming Wang, Chen Xiong +5Epidemic Forecasting Explainable Time Series Forecasting
Institute of Biomedicine and Health Engineering, Tsinghua Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, China · The First Hospital of Hebei Medical University, Shijiazhuang, China · The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China +1

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Deep Learning Network-Temporal Models For Traffic Prediction

Yufeng Xin, Ethan Fan

RENCI, University of North Carolina at Chapel Hill Chapel Hill, NC, USA

Multivariate Time Series Forecasting LLM-Based Time Series Forecasting Traffic Forecasting Spatiotemporal GNNs

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Accurate prediction of multivariate time series is essential for emerging network intelligent control, observability, and management functions. Existing statistical-based and shallow machine learning models have shown limited prediction capabilities on multivariate time series. They prioritize improvements in average prediction accuracy, while overlooking heterogeneous dependency structures and performance variability across individual time series. Recent advances in large language models have introduced new directions for multivariate time series forecasting; however, their application in conjunction with explicit structural dependency modeling remains relatively underexplored, especially in networked environments. In this paper, we present a topology-aware learning framework for large-scale network traffic prediction that explicitly models both temporal dynamics and structural dependencies in multivariate network time series. We first investigate a graph attention model designed to capture topology-induced correlations among network traffic time series. We then evaluate a fine-tuned large language model-based representations for improved generalization across heterogeneous traffic patterns. To further address the diversity of cross-correlations in high-dimensional traffic data, we introduce a clustering-based preprocessing stage that groups traffic flows with similar dependency characteristics prior to model training, reducing input complexity and improving learning stability. Experiments on real backbone traffic data show consistent improvements over statistical and recurrent neural network baselines. In addition to average accuracy, we evaluate performance across individual time series and observe reduced variability in prediction quality.

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Latest papers 64

Deep Learning Network-Temporal Models For Traffic Prediction

GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting

A Fast and Effective Solution to the Problem of Look-ahead Bias in LLMs

Auditable Context-Aware HFMD Forecasting with Structured LLM Agents