Time Series Forecasting

Latest papers 667

139 papers in the last four weeks, up 101% on the four weeks before. 0.9% of all new papers.

Learning Burst-Aware Early Warning Models for Capacity Stress under AI Workload Surges in Hyperscale Data Centers
Jun 19, 2026Zihan Yu, Xianling Zeng, Zhiming Xue +2Time Series Forecasting Cloud Computing
College of Professional Studies Northeastern University Boston, USA · College of Engineering Northeastern University Boston, USA · Khoury College of Computer Sciences Northeastern University Boston, USA
Diffusion-Driven State Space Models
Jun 19, 2026Jack Ruder, Michael WojnowiczLatent Dynamics Modeling Latent Diffusion Models
1Montana State University
Short-Term Electricity Demand Forecasting for New England Using a Hybrid Transformer-XGBoost Framework with Weather, Calendar, and COVID-19 Indicators
Jun 18, 2026Reza Ghanavati, Behrooz MosallaeiMultivariate Time Series Forecasting Concept Drift
Department of Chemical and Materials Engineering, New Mexico State University · Department of Electrical and Communications Engineering, New Jersey Institute of Technology
Self-Adaptive Scale Handling for Forecasting Time Series with Scale Heterogeneity
Jun 18, 2026Xu Zhang, Zhengang Huang, Yunzhi Wu +6Time Series Forecasting
College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China · Ant Group, Shanghai, China
Kolmogorov-Arnold Reservoir Computing
Jun 18, 2026Juntian Huang, Jürgen Kurths, Ying TangDynamical Systems Time Series Forecasting
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China · School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, China · Key Laboratory of Quantum Physics and Photonic Quantum Information, Ministry of Education, University of Electronic Science and Technology of China, Chengdu 611731, China +4
TelcoAgent: A Scalable 5G Multi-KPM Forecasting With 3GPP-Grounded Explainability
Jun 18, 2026Geon Kim, Dara Ron, Sukhdeep Singh +5Zero-Shot Time Series Forecasting Explainable Artificial Intelligence
†Kyung Hee University, South Korea · NextG Wireless Lab, North Carolina State University, USA · ‡Samsung R&D Institute India, Bangalore, India
Does Text Actually Help? Uncovering and Resolving Text Collapse in Multimodal Time Series Forecasting
Jun 17, 2026Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen +1Multivariate Time Series Forecasting Time Series Forecasting
Applied Artificial Intelligence Initiative Deakin University Geelong, Australia
MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes
Jun 17, 2026Nathaniel Jeffries, Miriam Wolff, Sam Royston +5Multivariate Time Series Forecasting Type 1 Diabetes
Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA · Replica Health, New York, NY, USA · Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA +1
TS-Fault: Benchmarking Time Series Forecasters Against Structural Faults
Jun 16, 2026Yuyang Zhao, Lian Xu, Hao Miao +2Time Series Forecasting Forecasting Benchmarks
Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · The University of Western Australia, Perth, Australia · Hong Kong Polytechnic University, Hong Kong, China +1
Do Time Series Foundation Model Benchmarks Hide Regime-Dependent Failures? Evidence from Traffic Speed Forecasting
Jun 16, 2026Yingshuo Wang, Xian Sun, Lingdong Kong +4Time Series Forecasting Forecasting Benchmarks
University of California, Berkeley, CA, USA · Duke University, Durham, NC, USA · National University of Singapore, Singapore +3
ConTex: Reformulating Counterfactual Generation For Time Series Forecasting
Jun 16, 2026Jan Voets, Hasan Tercan, Tobias Meisen +1Explainable Time Series Forecasting Counterfactual Explanations
Institute for Technologies and Management of Digital Transformation University of Wuppertal
Multiple cyclicity and Wavelet Decomposition with Channel Correlation for Long-term Time Series Forecasting
Jun 16, 2026Bin Wang, Heming Yang, Jinfang ShengMultivariate Time Series Forecasting Long-Term Time Series Forecasting
School of Computer Science and Engineering, Central South University, HuNan, China.
Delta-Based Target Reformulation for Short-Term Electricity Load Forecasting Using LSTM and Transformer Models
Jun 16, 2026Vansh BansalTime Series Forecasting Non-Stationary Time Series Forecasting
MorphStrata: Layer-Specific Perturbations for Generating Morphence Students in Time-Series Moving Target Defense
Jun 16, 2026Abhishek Bhardwaj, Arnav Doshi, Anusri Nagarajan +5Adversarial Attacks Time Series Forecasting
Department of Computer Science, San Jos´e State University, San Jos´e, CA, USA · Department of Computer Engineering, San Jos´e State University, San Jos´e, CA, USA · Department of Applied Data Science, San Jos´e State University, San Jos´e, CA, USA +1
HAMON: Passive Optical Sequence Mixing for Long-Horizon Forecasting
Jun 15, 2026Alper YıldırımLong-Term Time Series Forecasting Time Series Forecasting
RAID: Semantic Graph Diffusion for True Cold-Start and Cross-Lingual Forecasting
Jun 15, 2026Arunkumar V, Manoranjan Gandhudi, Gangadharan G. R. +2Time Series Forecasting Retrieval-Augmented Time Series Forecasting
University College of Engineering, Anna University Tiruchirappalli, Tamil Nadu, India · Central University of Karnataka, India · National Institute of Technology Tiruchirappalli, India +1
Neural dynamical systems on ferroelectric compute-in-memory for real-time forecasting
Jun 15, 2026Keshava Katti, Adithya Selvakumar, Pratik Chaudhari +1Efficient Neural Network Inference Dynamical Systems
Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
From Affect Prediction to Affect Forecasting: Evidence for Distinct Information Sources in Longitudinal Text
Jun 15, 2026Sadia Noor, Seemab Latif, Raja Khurram Shahzad +1Time Series Forecasting Valence-Arousal Modeling
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan · Department of Communication, Quality Management and Information Systems, Mid Sweden University, Ostersund, Sweden
Simulation-Augmented Multi-Step Split Conformal Prediction for Aggregated Forecasts
Jun 15, 2026Andro SabashviliUncertainty Quantification Time Series Forecasting
SciML in the Wild: A Diagnostic Study of When Structural Priors Help and When They Hurt
Jun 15, 2026Vrishank Sai Anand, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar +2Time Series Forecasting Scientific ML
GEMS Modern Academy Dubai, United Arab Emirates · Vizuara AI Labs, Pune, India
Robust Transformer-Based One-Step Stock Index Forecasting via Shifted Data Augmentation
Jun 14, 2026Tien Thanh ThachTime Series Forecasting Time Series Data Augmentation
Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Hybrid NARX-LLM for Greenland Iceberg Discharge: Prompt-Driven Residual Correction
Jun 13, 2026Yiquan Gao, Duohui XuZero-Shot Time Series Forecasting Residual Learning
1Heriot-Watt University. · 2StudioYG. · This work was completed before the first author’s affiliation with Heriot-Watt University and was conducted entirely independently of the institution.
Quantum-classical hybrid models based on error correction for time series forecasting
Jun 13, 2026Jonathan H. A. de Carvalho, Filipe C. de L. Duarte, Fernando M. de Paula Neto +1Time Series Forecasting Quantum Machine Learning
Centro de Inform´atica, Universidade Federal de Pernambuco, Recife, Brazil · Departamento de Finan¸cas e Contabilidade, Universidade Federal da Para´ıba, Jo˜ao Pessoa, Brazil
Machine Learning and the Random Walk Puzzle: Forecasting the CAD/USD Exchange Rate with Expanding Window Evaluation and SHAP Interpretability
Jun 13, 2026Louis Agyekum, Edmund Fosu Agyemang, Obu-Amoah Ampomah +6Time Series Forecasting Forecasting Benchmarks
Department of Economics, University of Ottawa, Ottawa, ON K1N 6N5, Canada · Department of Biostatistics and Data Science, Celia Scott Weatherhead School of Public Health and Tropical Medicine at Tulane University, New Orleans, LA 70112, USA · School of Mathematical and Statistical Sciences, University of Texas Rio Grande Valley, Edinburg, Texas, USA +6
Semantics-Enhanced Retrieval-Augmented Time Series Forecasting
Jun 12, 2026Shiqiao Zhou, Zipeng Wu, Holger Schöner +3Time Series Forecasting Non-Stationary Time Series Forecasting
School of Computer Science, University of Birmingham, Birmingham, United Kingdom · School of Mathematics, University of Birmingham, Birmingham, United Kingdom · Siemens AG, Munich, Germany +1
A Comparative Study of Deep Learning Architectures for Multi-Horizon Behavioural Forecasting for Mobile Health
Jun 12, 2026Pavlos Nicolaou, Kleanthis Malialis, Artemis Kontou +1Zero-Shot Time Series Forecasting Time Series Forecasting
KIOS Research and Innovation Center of Excellence · Department of Computer Science University of Cyprus, Nicosia, Cyprus
Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Models
Jun 12, 2026Xilin Dai, Yiding Liu, Hongjie Xia +4Test-Time Adaptation Residual Learning
Ant International · The Chinese University of Hong Kong
Can Machine Learning Forecast Rice Yields in Data-Constrained Settings? Satellite Climate Data, National Crop Statistics, and Lessons from Sierra Leone
Jun 11, 2026Ibrahim Denis FofanahTime Series Forecasting Spatiotemporal Forecasting
Seidenberg School of Computer Science & Information Systems, Pace University, New York, USA · RiseAfrica Foundation for STEM and Innovation, Sierra Leone, West Africa
Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecasting
Jun 11, 2026Yifan Hu, Hongzhou Chen, Peiyuan Liu +3Irregular Time-Series Modeling Time Series Forecasting
Ant International
Once-for-All: Scalable Simultaneous Forecasting via Equilibrium State Estimation
Jun 11, 2026Beinan Xu, Andy Song, Jiti Gao +1Multivariate Time Series Forecasting Time Series Forecasting
School of Computing Technologies, RMIT University, Melbourne, Australia · Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia · School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

Jun 19, 2026J/K move · Enter open · S save

Learning Burst-Aware Early Warning Models for Capacity Stress under AI Workload Surges in Hyperscale Data Centers

Zihan Yu, Xianling Zeng, Zhiming Xue, Yalun Qi+1

College of Professional Studies Northeastern University Boston, USA · College of Engineering Northeastern University Boston, USA · Khoury College of Computer Sciences Northeastern University Boston, USA

Time Series Forecasting Cloud Computing

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The rapid growth of large-scale AI workloads, particularly Large Language Model (LLM) training and inference, is fundamentally reshaping the operational dynamics of hyperscale data centers. Unlike traditional cloud workloads, AI-driven jobs exhibit bursty, high-intensity, and rapidly shifting resource demands, often leading to sudden capacity stress that cannot be effectively handled by reactive threshold-based mechanisms. In this paper, we propose a deployment-oriented, burst-aware early warning framework for proactive capacity stress prediction under AI workload surges. We formulate the problem as a high-recall forecasting task over multivariate telemetry windows, with the explicit goal of enabling operational intervention before system degradation occurs. The proposed framework integrates workload intensity, temporal variation, and system pressure signals, and employs a lightweight tree-based learning model to capture nonlinear interactions in highly imbalanced environments. To evaluate the system under realistic conditions, we introduce an AI workload surge injection methodology that simulates burst-driven demand patterns observed in large-scale AI systems. Our XGBoost-based model achieves an ROC AUC of 0.697 and an AP of 0.670, significantly outperforming baseline methods. Under deployment-oriented threshold selection, the framework achieves a Recall of 0.914, enabling the detection of the majority of stress-prone periods with acceptable false-alarm cost. Beyond predictive performance, we show how the proposed framework can be integrated into operational control loops to support proactive actions such as workload throttling and resource scaling. Our results highlight the practical value of high-recall, learning-based early warning systems in enabling resilient and adaptive data center operations in the era of AI-driven workloads.