Time Series Forecasting

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

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The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle
Mar 18, 2026Dibakar SigdelTransformer Time Series Forecasting
Mindverse Computing LLC, WA 98087
Not All Retrievals are Useful: Cross-Attention for Input-Aware RAG in Time Series Forecasting
Mar 16, 2026Seunghan Lee, Jaehoon Lee, Jun Seo +7Zero-Shot Time Series Forecasting Cross-Attention
LG AI Research Seoul, South Korea
Competition-Aware CPC Forecasting with Near-Market Coverage
Mar 13, 2026Sebastian Frey, Edoardo Beccari, Maximilian Kranz +4Competitive Analysis Online Advertising
*Nova School of Business and Economics, Portugal · †University of Hamburg, Germany
Overcoming the Modality Gap in Context-Aided Forecasting
Mar 12, 2026Vincent Zhihao Zheng, Étienne Marcotte, Arjun Ashok +4Synthetic Data Augmentation Multimodal Pretraining
ServiceNow AI Research, Montréal, Canada · Department of Civil Engineering, McGill University, Montréal, Canada · DIRO, Université de Montréal, Montréal, Canada +1
SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms
Mar 5, 2026Longkun Xu, Xiaochun Zhang, Qiantu Tuo +1Time Series Forecasting Code Generation
Ecoflow Inc.
Learning with the Nash-Sutcliffe loss
Mar 1, 2026Hristos Tyralis, Georgia PapacharalampousMultivariate Time Series Forecasting Time Series Forecasting
1Support Command, Hellenic Air Force, Elefsina Air Base, 19 200, Elefsina, Greece · Department of Land, Environment, Agriculture and Forestry, University of Padova, Viale dell'Università 16, 35020, Legnaro, Italy
Adaptive Automatic Model Selection for Demand Forecasting under Heterogeneous Demand Patterns
Feb 15, 2026Adolfo González, Víctor ParadaTime Series Forecasting Forecasting Benchmarks
Department of Computer Engineering and Informatics, Faculty of Engineering, University of Santiago of Chile, Santiago, Chile
Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles
Feb 13, 2026Max Bruninx, Diederik van Binsbergen, Timothy Verstraeten +2Gradient-Boosted Decision Trees Ensemble Learning
Vrije Universiteit Brussel, Brussels, Belgium · Norwegian University of Science and Technology, Trondheim, Norway
DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters
Feb 6, 2026Haoran Zhang, Haixuan Liu, Yong Liu +4Time Series Forecasting Probabilistic Time Series Forecasting
School of Software, BNRist, Tsinghua University
Symmetric Composition of Anisotropic Operators for Global Subseasonal-to-Seasonal Climate Forecasting
Feb 4, 2026Ziyu Zhou, Yuchen Fang, Weilin Ruan +4Climate Science Time Series Forecasting
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · DAMO Academy, Alibaba Group, Hangzhou, China · The Hong Kong University of Science and Technology, Hong Kong SAR, China
Spectral Text Fusion: A Frequency-Aware Approach to Multimodal Time-Series Forecasting
Feb 2, 2026Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen +1Multivariate Time Series Forecasting Time Series Forecasting
Applied Artificial Intelligence Initiative Deakin University Geelong, Australia
Parametric and Generative Forecasts of EPEX Day\char45 Ahead Energy Market Curves
Jan 28, 2026Julian Gutierrez, Redouane SilventeTime Series Forecasting Probabilistic Forecasting
New York University Abu Dhabi, Saadiyat Island, PO Box 129188, Abu Dhabi, United Arab Emirates · CREST, ENSAE, Institut Polytechnique de Paris, 5 Avenue Henry Le Chatelier 91120 Palaiseau, France
Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting
Jan 23, 2026Haonan Yang, Jianchao Tang, Zhuo LiDisentangled Representation Learning Prototype Learning
National University of Defense Technology, Changsha, Hunan, China
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
Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings
Jan 4, 2026Pengfei Qu, Wenyu Ouyang, Chi Zhang +6Time Series Forecasting
School of Computer Science and Technology, Dalian University of Technology, Dalian, China · School of Infrastructure Engineering, Dalian University of Technology, Dalian, China · School of Information and Communication Engineering, Dalian University of Technology, Dalian, China +2
Time Series Foundation Models for Process Model Forecasting
Dec 8, 2025Yongbo Yu, Jari Peeperkorn, Johannes De Smedt +1Zero-Shot Time Series Forecasting Process Mining
Research Center for Information Systems Engineering (LIRIS), KU Leuven, Belgium
Weighted Contrastive Learning for Anomaly-Aware Time-Series Forecasting
Dec 8, 2025Joel Ekstrand, Tor Mattsson, Zahra Taghiyarrenani +3Multivariate Time Series Forecasting Contrastive Learning
Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden · Mikael Linden Consulting AB, Stockholm, Sweden
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
KAN vs LSTM Performance in Time Series Forecasting
Nov 23, 2025Tabish Ali Rather, S M Mahmudul Hasan Joy, Nadezda Sukhorukova +1Time Series Forecasting Non-Stationary Time Series Forecasting
Department of Mathematics, Swinburne University of Technology, Hawthorn, 3122, Victoria, Australia.
LOBERT: Generative AI Foundation Model for Limit Order Book Messages
Nov 16, 2025Eljas Linna, Kestutis Baltakys, Alexandros Iosifidis +1Quantitative Finance Time Series Forecasting
Data Science Research Centre Tampere University
DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment
Oct 28, 2025Hao Wang, Licheng Pan, Yuan Lu +7Time Series Forecasting Optimal Transport
Xiaohongshu Inc. · College of Control Science and Technology, Zhejiang University · College of Computer Science and Technology, Zhejiang University +4
Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models
Oct 28, 2025Hao Wang, Licheng Pan, Yuan Lu +7Time Series Forecasting Adaptive Loss Weighting
Xiaohongshu Inc. · State Key Lab of General AI, School of Intelligence Science and Technology, Peking University · College of Engineering, Purdue University +6
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting
Oct 1, 2025Nouha Karaouli, Denis Coquenet, Elisa Fromont +2Time Series Forecasting Time-Series Foundation Models
Univ. Rennes, CNRS, Inria, IRISA - UMR 6074, F-35000 Rennes, France · Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LPNC, Grenoble, France · Univ. Grenoble Alpes, CEA, LIST, 38000 Grenoble, France
fev-bench: A Realistic Benchmark for Time Series Forecasting
Sep 30, 2025Oleksandr Shchur, Abdul Fatir Ansari, Caner Turkmen +5Benchmark Design Time Series Forecasting
AWS · Keystone AI · Prior Labs +2
Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models
Sep 30, 2025Kun Feng, Shaocheng Lan, Yuchen Fang +6Rotary Positional Embeddings Zero-Shot Time Series Forecasting
School of Information Science and Technology, ShanghaiTech University, Shanghai, China · Ant Group, Shanghai, China
DMSC: Dynamic Multi-Scale Coordination Framework for Time Series Forecasting
Aug 3, 2025Haonan Yang, Jianchao Tang, Zhuo Li +1Time Series Forecasting Multi-Scale Feature Fusion
National University of Defense Technology, Changsha, Hunan, China
Temporal horizons in forecasting: a performance-learnability trade-off
Jun 4, 2025Pau Vilimelis Aceituno, Jack William Miller, Noah Marti +2Long-Term Time Series Forecasting Time Series Forecasting
Institute of Neuroinformatics, ETH Zürich and University of Zürich, Winterhurerstrasse 190, Zürich 8057, Switzerland · School of Computing, National Australian University, 108 North Rd, Acton ACT 2601, Australia · Unit of Land Change Science, Swiss Federal Research Institute for Forest, Snow and Landscape Zürcherstrasse 111, Birmensdorf 8903, Switzerland
Time-o1: Time-Series Forecasting Needs Transformed Label Alignment
May 23, 2025Hao Wang, Licheng Pan, Zhichao Chen +5Long-Term Time Series Forecasting Time Series Forecasting
Xiaohongshu Inc. · State Key Lab of General AI, School of Intelligence Science and Technology, Peking University · Gaoling School of Artificial Intelligence, Renmin University of China +4
Fewer yet critical: Reducing Redundant Token Dependencies for Transformer-based Time Series Forecasting
Mar 10, 2025Jianqi Zhang, Yuchan Liu, Zeen Song +2Time Series Forecasting Transformer Attention
National Key Laboratory of Space Integrated Information Systems, Institute of Software, Chinese Academy of Sciences, Beijing, China 1 · University of Chinese Academy of Sciences, Beijing, China 2 · Digital-Intelligent Operations Center, Guizhou Power Grid Co., Ltd., Guizhou, China 3 +1
Surrogate Modeling for Explainable Predictive Time Series Corrections
Dec 27, 2024Alfredo Lopez, Florian SobieczkySurrogate Modeling Explainable Time Series Forecasting
Software Competence Center Hagenberg, Hagenberg, Austria.

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The Phasor Transformer: Resolving Attention Bottlenecks on the Unit Circle

Dibakar Sigdel

Mindverse Computing LLC, WA 98087

Transformer Time Series Forecasting Time Series Transformers

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Transformer models have redefined sequence learning, yet dot-product self-attention introduces a quadratic token-mixing bottleneck for long-context time-series. We introduce the Phasor Transformer block, a phase-native alternative representing sequence states on the unit-circle manifold

S^1

. Each block combines lightweight trainable phase-shifts with parameter-free Discrete Fourier Transform (DFT) token coupling, achieving global

\mathcal{O}(N\log N)

mixing without explicit attention maps. Stacking these blocks defines the Large Phasor Model (LPM). We validate LPM on autoregressive time-series prediction over synthetic multi-frequency benchmarks against honest baselines: it beats a zero-parameter persistence baseline and, with the corrected gradient path, improves monotonically with depth before saturating, while remaining competitive-but-not-superior to self-attention at a fraction of the parameter count. Our results establish an explicit efficiency--accuracy frontier, showing that scalable temporal modeling in oscillatory domains can emerge from geometry-constrained phase computation with deterministic global coupling.