Time Series Forecasting

Follow this topic Ask about this topic

Latest papers 661

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

All topics

AdaptLSTM: Efficient Adaptive Online Learning for Cloud Workload Forecasting under Distribution Drift
Oct 8, 2026Xinhua Miao, Bowei Yang, Zhengong CaiOnline Learning Adaptive Learning
Zhejiang University Ningbo, China · Zhejiang University Hangzhou, China
AdaCast: Conditional Parameter Generation for Adaptive Time Series Forecasting
Oct 8, 2026Darahaas Nallagatla, Darryl Cherian Jacob, Pan HeTime Series Forecasting Time-Series Foundation Models
Auburn University
Training on the Future: A Delay-Aware Audit of Test-Time Adaptation for Time-Series Forecasting
Oct 8, 2026Mohamed Readh Fentazi, Mazene Ameur, Adlen KsentiniTest-Time Adaptation Time Series Forecasting
Communication Systems Department EURECOM Sophia Antipolis, France
Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting
Oct 8, 2026Jack Waller, Xing Liang, Dimitrios Makris +1Quantum Neural Networks Quantum Machine Learning
School of Computer Science and Mathematics, Kingston University London, London, KT1 2EE, U.K.. · Department of Electronics and Electrical Engineering, Brunel University of London, London, UB8 3PH, U.K..
PulseBound: Future-Beat State Forecasting Under an Explicit Information Boundary
Oct 8, 2026Chenyang Xu, Donglin Xie, Xi Xiang +12Predictive Representation Learning Representation Learning
OPPO Health Lab, Guangdong OPPO Mobile Telecommunications Corp., Ltd. · Xidian University · Peking University +3
Revisiting Identity and Spectra Dispersion in Media-Bridged Time Series Forecasting: Linking Multivariate Signals and Narrative Flows
Oct 8, 2026Jierui Lei, Wenjian Zhang, Qingyi Yang +5Time Series Forecasting Spatiotemporal Forecasting
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China · State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, Guangdong 518107, China +3
Sera: Semantic Representation Aggregation for Reliable and Interpretable Battery Health Forecasting
Oct 8, 2026Jiawei Li, Fang Liu, Wei Zhang +3Time Series Forecasting Explainable Time Series Forecasting
Infocomm Technology Cluster Singapore Institute of Technology Singapore · School of Science and Technology Singapore University of Social Sciences Singapore · College of Smart Energy Shanghai Jiao Tong University Shanghai, China +2
AliO: Output Alignment Matters in Long-Term Time Series Forecasing
Oct 8, 2026Kwangryeol Park, Jaeho Kim, Seulki LeeLong-Term Time Series Forecasting Time Series Forecasting
Artificial Intelligence Graduate School Ulsan National Institute of Science & Technology (UNIST), South Korea · Artificial Intelligence Korea University, South Korea · Department of Computer Science and Engineering Ulsan National Institute of Science & Technology (UNIST), South Korea
SACQ: Structured Decoding with Memory-Conditioned Refinement for Long-Horizon Forecasting
Oct 8, 2026Guo Cheng, Zhengzhuo Xu, Chenchen Jing +1Long-Term Time Series Forecasting Time Series Forecasting
University of Science and Technology Beijing, China · Zhejiang University of Technology, Hangzhou, China · Institution of Artificial Intelligence, University of Science and Technology Beijing, China
RideBench: A Large-Scale Exogenous-Aware Benchmark for Ride-Hailing Time Series Forecasting
Oct 8, 2026Shengsheng Lin, Jing Hu, Zhengyang Hu +8Time Series Forecasting Forecasting Benchmarks
South China University of Technology, China · Didi Chuxing, China · The University of Hong Kong, China
Deep Learning vs. Statistical Models for Multi-Horizon Price Forecasting of Second-Hand Electronics: A Systematic Benchmark
Oct 7, 2026Mateusz Buczyński, Michał Woźniak, Konrad Kaczyński +2Time Series Forecasting Forecasting Benchmarks
Faculty of Economic Sciences, University of Warsaw, Dluga 44/50, Warsaw, 00-241, PL · WeSub, Branickiego 15, Warsaw, 02-972, PL · Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, Warsaw, 00-662, PL
On linearity or non-linearity in machine learning for quantum chaotic dynamics
Oct 7, 2026Francesco Perciavalle, Agostino Gallo, Francesco Plastina +3Chaotic Time-Series Prediction Time Series Forecasting
Dipartimento di Fisica, Universit`a della Calabria, Rende 87036, Italy. · INFN, Sezione LNF, gruppo collegato di Cosenza. · Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy.
HAN-Mamba: Hierarchical Selective State Space Networks for Multi-Scale Financial Volatility Forecasting
Oct 7, 2026Mihai Bogdan Deaconu, Ioan Daniel PopTime Series Forecasting Financial Time Series
Faculty of Mathematics and Computer Science, Babeş-Bolyai University, Cluj-Napoca, Romania
Evaluating Sequence Assembly Strategies for Differentially Private Synthetic Time-Series Forecasting
Oct 7, 2026Guoxiong Long, Huizhen Huang, Qikun Cai +2Synthetic Data Evaluation Synthetic Data Generation
School of Computer and Big Data, Minjiang University, No. 200 Xiyuangong Road, Fuzhou, 350108, Fujian, China
Evolve on the Host, Predict on the Edge: Deploying Online Neuroevolutionary Architecture Search for Cross-sectional Stock Return Prediction
Oct 7, 2026Jonathan Chang, Zimeng LyuTime Series Forecasting Online Learning
Union County Magnet High School · Department of Computer Science and Technology, Kean University
Matching of signal, noise and hardware timescales for filtering and forecasting of correlated noise signals
Oct 7, 2026Joshua Donald, Alex Gabbitas, Arthur G. T. Coveney +2Reservoir Computing Time Series Forecasting
Department of Physics, Loughborough University, Loughborough, LE11 3TU, United Kingdom
Temporal Predictive Multiplicity: Equally Accurate Time Series Models Yield Different Forecast Trajectories
Oct 7, 2026Emanuele Albini, Francesca Toni, Saumitra Mishra +1Time Series Forecasting
J.P. Morgan AI Research Imperial College London London, UK · Imperial College London London, UK · J.P. Morgan AI Research London, UK
DSTNet: Dynamic Spectral Trajectory Network for Causal Multi-Horizon Financial Forecasting
Oct 7, 2026Aashish Bohra, Lokendra VishwakarmFinancial Forecasting Time Series Forecasting
Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan 342030, India · School of Computing and Data Sciences, FLAME University, Pune Maharashtra, India
Are We Really Benchmarking Forecasting Models? The Impact of Preprocessing on Time Series Performance
Oct 6, 2026Guilherme Afonso Galindo Padilha, Paulo Salgado Gomes de Mattos Neto, Rafael Menelau Oliveira e CruzTime Series Forecasting Forecasting Benchmarks
LIVIA, Department of Software and IT Engineering École de technologie supérieure (ÉTS) Montreal, QC, Canada · Center for Informatics (CIn) Universidade Federal de Pernambuco (UFPE) Recife, PE, Brazil
Machine Learning for German Redispatch Forecasting under Data Delays and Temporal Distribution Shift
Oct 6, 2026Faraz Shamim, Faris ShamimDistribution Shift Robustness Time Series Forecasting
KIST Medical College and Teaching Hospital, Nepal · OTH Regensburg
Forecast Accuracy Is Not Trading Profit: Evolving Small Recurrent Networks for Stock Return Prediction
Oct 6, 2026Jonathan Chang, Zimeng LyuTime Series Forecasting Recurrent Neural Networks
Union County Magnet High School Scotch Plains, New Jersey, USA · Kean University Union, New Jersey, USA
Active Feature Acquisition for Cost-Efficient Temporal Prediction with Reduced Participant Burden
Oct 5, 2026Yunni Qu, Bing Cai Kok, Whitney Ringwald +4Time Series Forecasting Active Learning
Department of Computer Science, University of North Carolina at Chapel Hill · Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill · School of Social Sciences, Nanyang Technological University, Singapore +2
StaFIR: Convex Learning of Stationarity-Aware Causal Filters
Oct 5, 2026Lorena Egger, Mathis LingerTime Series Forecasting Financial Time Series
Drakai Capital Paris
Scale-Invariant Training for Time Series Foundation Models
Oct 5, 2026Ignacy Stepka, Willa Potosnak, Kin G. Olivares +1Time Series Forecasting Invariant Representation Learning
Carnegie Mellon University · Amazon · Nixtla
Benchmarking Time Series Foundation Models for Load Forecasting Under Covariate Uncertainty
Oct 5, 2026Tomas Kaljevic, Ivan Arzola, Yu ZhangZero-Shot Time Series Forecasting Time Series Forecasting
Department of Electrical and Computer Engineering University of California, Santa Cruz
Forecasting the Growth of Social Media Information Cascades: Towards Human-in-the-Loop Misinformation Triage
Oct 5, 2026Ansh Gupta, Abhiram Gorle, Aayush Rajesh +1Social Media Analysis Time Series Forecasting
The Overlake School · Stanford University
Can LLM-assisted regularization increase forecast accuracy for migration flows in low data regimes?
Oct 5, 2026Nathaniel T. Hindman, Fabricio MuraiTime Series Forecasting LLM-Based Time Series Forecasting
Artificial Intelligence Worcester Polytechnic Institute Worcester, Massachusetts · Data Science Worcester Polytechnic Institute Worcester, Massachusetts
FreSia: Frequency-Semantic Instantiation and Alignment for Multivariate Time Series Analysis
Oct 5, 2026Yubo Wang, Hui He, Hezhe Qiao +2Multivariate Time Series Forecasting Time Series Forecasting
School of Computer Science and Technology, Beijing Institute of Technology, No.5 Zhongguancun South Street, Beijing, 100081, China · School of Computing and Information Systems, Singapore Management University, 80 Stamford Road, 178902, Singapore
The Premise Is the Problem: Exchangeability Failure in Self-Monitored Test-Time Adaptation
Oct 4, 2026Weijia Han, Lisha Qu, Zhenda Li +1Test-Time Adaptation Continual Test-Time Adaptation
What Does an Observability Foundation Model Know?
Oct 4, 2026Dhyey Dharmendrakumar Mavani, Rian Atri, Tairan JiTime Series Forecasting Representation Probing
Independent · Keiji AI

Oct 8, 2026, cs.LGJ/K move · Enter open · S save

AdaptLSTM: Efficient Adaptive Online Learning for Cloud Workload Forecasting under Distribution Drift

Xinhua Miao, Bowei Yang, Zhengong Cai

Zhejiang University Ningbo, China · Zhejiang University Hangzhou, China

Online Learning Adaptive Learning Time Series Forecasting

Open paper AskPDF arXiv

Accurate workload forecasting is critical for elastic resource provisioning in web-scale cloud services, where distribution shifts driven by viral content, product launches, and user behavior degrade offline-trained models rapidly. Naive online learning recovers accuracy but incurs prohibitive per-step compute cost. We propose AdaptLSTM, an adaptive online framework that detects drift via validation-calibrated thresholds and applies selective, targeted updates. On the Alibaba Machine Trace, AdaptLSTM recovers 54% of Naive Online's improvement at 20% cost (

2.7\times

efficiency,

p=0.002

over 10 seeds). On the more volatile Container Trace, it achieves 96% at 20% cost (

4.8\times

efficiency,

+75\%

MAE reduction over Static). Unlike classical drift detectors (ADWIN, DDM, Page-Hinkley) which fail to trigger on regression-scale error streams, AdaptLSTM fires 42 times over 301 steps and outperforms matched-budget baselines. Wall-clock profiling shows

1.33\times

throughput gain and 45% update-time reduction. The framework is model-agnostic: identical Pareto patterns hold for LSTM, GRU, and Transformer backbones.