Time-series forecasting
This page was last updated on 2026-08-10 06:37:27 UTC
Manually curated articles on Time-series forecasting
| Abstract | Title | Authors | Publication Date | Journal/ Conference | Citation count | Highest h-index | View recommendations |
|---|---|---|---|---|---|---|---|
| visibility_off | A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection | Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, C. Alippi, G. I. Webb, Irwin King, Shirui Pan | 2023-07-07 | IEEE Transactions on Pattern Analysis and Machine Intelligence | 519 | 56 | open_in_new |
| visibility_off | Graph-Guided Network for Irregularly Sampled Multivariate Time Series | Xiang Zhang, M. Zeman, Theodoros Tsiligkaridis, M. Zitnik | 2021-10-11 | ArXiv, International Conference on Learning Representations | 183 | 62 | open_in_new |
| visibility_off | Taming Local Effects in Graph-based Spatiotemporal Forecasting | Andrea Cini, Ivan Marisca, Daniele Zambon, C. Alippi | 2023-02-08 | ArXiv, Neural Information Processing Systems | 57 | 56 | open_in_new |
| visibility_off | Sparse Graph Learning from Spatiotemporal Time Series | Andrea Cini, Daniele Zambon, C. Alippi | 2022-05-26 | Journal of machine learning research, J. Mach. Learn. Res. | 37 | 56 | open_in_new |
| visibility_off | Graph Deep Learning for Time Series Forecasting | Andrea Cini, Ivan Marisca, Daniele Zambon, C. Alippi | 2023-10-24 | ACM Computing Surveys | 52 | 56 | open_in_new |
| visibility_off | Large Language Models Are Zero-Shot Time Series Forecasters | N. Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson | 2023-10-11 | ArXiv, Neural Information Processing Systems | 812 | 19 | open_in_new |
| visibility_off | Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces | Chloe Wang, Oleksii Tsepa, Jun Ma, Bo Wang | 2024-02-01 | ArXiv, arXiv.org | 187 | 10 | open_in_new |
| visibility_off | A decoder-only foundation model for time-series forecasting | Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou | 2023-10-14 | ArXiv, DBLP | 811 | 16 | open_in_new |
| visibility_off | Unified Training of Universal Time Series Forecasting Transformers | Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo | 2024-02-04 | ArXiv, DBLP | 685 | 36 | open_in_new |
| visibility_off | Time-LLM: Time Series Forecasting by Reprogramming Large Language Models | Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, X. Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen | 2023-10-03 | ArXiv, International Conference on Learning Representations | 1102 | 15 | open_in_new |
| visibility_off | Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series | Vijay Ekambaram, A. Jati, Nam H. Nguyen, Pankaj Dayama, Chandra Reddy, Wesley M. Gifford, Jayant Kalagnanam | 2024-01-08 | ArXiv, Neural Information Processing Systems | 173 | 14 | open_in_new |
| visibility_off | Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency | Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, M. Zitnik | 2022-06-17 | ArXiv, Neural Information Processing Systems | 520 | 62 | open_in_new |
| visibility_off | Domain Adaptation for Time Series Under Feature and Label Shifts | Huan He, Owen Queen, Teddy Koker, Consuelo Cuevas, Theodoros Tsiligkaridis, M. Zitnik | 2023-02-06 | ArXiv, DBLP | 137 | 62 | open_in_new |
| visibility_off | AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs | Daniele Zambon, C. Alippi | None | Neural Information Processing Systems, Advances in Neural Information Processing Systems 35 | 9 | 56 | open_in_new |
| visibility_off | Graph State-Space Models and Latent Relational Inference | Daniele Zambon, Andrea Cini, L. Livi, C. Alippi | 2023-01-04 | ArXiv | 8 | 56 | open_in_new |
| visibility_off | UniTS: A Unified Multi-Task Time Series Model | Shanghua Gao, Teddy Koker, Owen Queen, Thomas Hartvigsen, Theodoros Tsiligkaridis, M. Zitnik | 2024-02-29 | Neural Information Processing Systems, Advances in Neural Information Processing Systems 37 | 130 | 62 | open_in_new |
| Abstract | Title | Authors | Publication Date | Journal/ Conference | Citation count | Highest h-index | View recommendations |
Recommended articles on Time-series forecasting
| Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |
|---|---|---|---|---|---|---|
| visibility_off | Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting | Morad Laglil, Bertrand Pracca, Emilie Devijver, Éric Gaussier | 2026-07-25 | ArXiv | 0 | 14 |
| visibility_off | TS-RAG: Retrieval Augmented Generation for Time Series Forecasting | Yixiong Xiao, Congxi Xiao, Jingbo Zhou | 2026-08-06 | ArXiv | 1 | 5 |
| visibility_off | A survey of deep time series forecasting backbone architectures: progress, pitfalls, and a systematic comparison | Xiang Li, Yanping Zheng, Zhewei Wei | 2026-07-08 | Frontiers of Computer Science | 0 | 6 |
| visibility_off | Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis | Yisong Fu, Zezhi Shao, Chengqing Yu, Yujie Li, Yongjun Xu, Xueqi Cheng, Fei Wang | 2026-07-02 | ArXiv | 0 | 16 |
| visibility_off | SDformer: Fusing Series Decomposition for Superior Long-Term Time Series Forecasting | Jiayi Li, Zihang Zhang, Chao Zhang, Jun Tang, Shangce Gao | 2026-07-01 | IEEE/CAA Journal of Automatica Sinica | 0 | 16 |
| visibility_off | RhyMix: A Lightweight Adaptive Multi-Rhythm Network for Long-Term Time Series Forecasting | Sumit S. Shevtekar, Chandresh Kumar Maurya | 2026-07-09 | ArXiv | 0 | 8 |
| visibility_off | A Hybrid Spatiotemporal Framework with Memory and Diffusion Convolution for Traffic Flow Prediction | Xi Chen, Jiajia Chen | 2026-07-20 | Applied Artificial Intelligence Research | 0 | 0 |
| visibility_off | Extreme Adaptive Transformer for Time Series Forecasting | Sanjeev Shrestha, Hui Liu, Yifan Zhang | 2026-07-02 | ArXiv | 0 | 4 |
| visibility_off | DFG-PatchTST: a dynamic fusion gated patch transformer for multicomponent time series forecasting | Yunsen Zhou, Yinxin Bao, Quan Shi | 2026-07-01 | Applied Intelligence | 0 | 7 |
| visibility_off | Sample-efficient fine-tuning with textual prompts for time series forecasting | Kaibin Wei, Jianqiang Jing, Jiawei Liu, Qing Liu, Xiannian Xie | 2026-07-08 | PLOS One | 0 | 3 |
| visibility_off | Distribution-Aware Diffusion-LLM for Robust Ultra-Long-Term Time Series Forecasting | Falguni Ghosh, Vahid Hashemi, Bernhard Kainz | 2026-06-22 | ArXiv | 0 | 2 |
| visibility_off | Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting | Alireza Jafari, Judy Fox, Geoffrey C. Fox, M. Marathe, A. Adiga | 2026-06-17 | ArXiv | 0 | 58 |
| visibility_off | TimEE: End-to-end Time Series Classification via In-Context Learning | Jaris Kuken, Shi Bin Hoo, M. Mráz, Frank Hutter, Lennart Purucker | 2026-07-08 | ArXiv | 0 | 10 |
| visibility_off | Learning Spatio-Temporal Foundation Models from Pure Synthetic Data | Yutong Feng, Shiyuan Piao, Yutong Xia, Xu Liu, Wenqi Fan, F. Tsung, See-Kiong Ng, Yuxuan Liang | 2026-06-27 | ArXiv | 0 | 49 |
| visibility_off | RAID: Semantic Graph Diffusion for True Cold-Start and Cross-Lingual Forecasting | V. Arunkumar, Manoranjan Gandhudi, R. GangadharanG., A. Prakash, S. Senthilkumar | 2026-06-15 | ArXiv | 0 | 4 |
| visibility_off | PMDformer: Patch-Mean Decoupling Information Transformer for Long-term Forecasting | Ao Hu, Liangjian Wen, Jiang Duan, Yong Dai, He Yan, Dongkai Wang, Jun Wang, Yukun Zhang, Ruoxi Jiang, Zenglin Xu | 2026-06-25 | ArXiv | 1 | 7 |
| visibility_off | GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets | Janak M. Patel, A. Deodhar, Dagnachew Birru | 2026-06-26 | ArXiv | 0 | 7 |
| visibility_off | STKAN: Kolmogorov-Arnold Networks for Spatio-Temporal Forecasting | Sicong Lai, Yuehong Hu, Siru Zhong, Sichao Qiao, Yuxuan Liang, Guangyin Jin | 2026-07-14 | ArXiv | 0 | 9 |
| visibility_off | Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Models | Xilin Dai, Yiding Liu, Hongjie Xia, Yifan Hu, Zewei Dong, Jiangnan Yang, Qiang Xu | 2026-06-12 | ArXiv | 1 | 17 |
| visibility_off | Speaking Numbers to LLMs: Multi-Wavelet Number Embeddings for Time Series Forecasting | Defu Cao, Zijie Lei, Muyan Weng, Jiao Sun, Yan Liu | 2026-06-25 | ArXiv | 2 | 14 |
| visibility_off | Transformer-Based Architectures for Machinery Prognostics: A Review | Maxime Pierfederici, M. Jha, Chetan S. Kulkarni, D. Theilliol | 2026-07-03 | PHM Society European Conference | 0 | 12 |
| visibility_off | MFSNet: lightweight multi-scale MLP-guided frequency suppression network for multivariate time series forecasting | Yaling Xun, Jiaxin Dou, Haifeng Yang, Jianghui Cai, Xing Wang | 2026-07-31 | International Journal of Machine Learning and Cybernetics | 0 | 16 |
| visibility_off | Self-Gating Attention for Efficient Time Series Forecasting | Dezheng Wang, Tong Chen, Wei Yuan, Congyan Chen, Shihua Li, Hongzhi Yin | 2026-07-02 | IEEE Transactions on Industrial Informatics | 0 | 86 |
| visibility_off | HyBDM: Multi-Scale Hybrid Experts for Time Series Forecasting with Bidirectional Dependency Modeling | Wenqiang Ma, Chen Cheng, Xue Cheng, Jiarui Ye | 2026-07-18 | ArXiv | 0 | 3 |
| visibility_off | Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space | Di Wu, Huan Liu, Zhixiang Chi, Yuanhao Yu, Konstantinos N. Plataniotis, Yang Wang | 2026-07-02 | Trans. Mach. Learn. Res. | 0 | 14 |
| visibility_off | SOHET: Sequence Of Heterogeneous Events Transformer with Self-Supervised Pre-Training | Kees Jan de Vries, M. Radha, Mathijs de Jong | 2026-06-19 | ArXiv | 0 | 9 |
| visibility_off | Robust Transformer-Based One-Step Stock Index Forecasting via Shifted Data Augmentation | T. Thach | 2026-06-14 | ArXiv | 0 | 6 |
| visibility_off | EvoTS: Evolutionary Transformer Search for Time Series Forecasting | A. Elsaid, Damir Pulatov | 2026-06-30 | Proceedings of the Genetic and Evolutionary Computation Conference | 0 | 12 |
| visibility_off | NeuroFlexMLP: A Low Complexity MLP Architecture for Long-Term Time Series Forecasting | P. F. Pérez, Claudio Fiandrino, Marco Fiore, Joerg Widmer | 2026-07-01 | 2026 Mediterranean Artificial Intelligence and Networking Conference (MAIN) | 0 | 10 |
| visibility_off | Towards a Unified Generative Model for Scarce Time Series with Domain Experts | Zihao Yao, Qijian Zheng, Jian-yong Zuo, Yaying Zhang | 2026-06-13 | ArXiv | 0 | 3 |
| visibility_off | Leveraging Time Series Foundation Models Embeddings for Remaining Useful Life Prediction | Ilias Abdouni, Alexandre Voisin, Christophe Cerisara | 2026-07-03 | PHM Society European Conference | 0 | 4 |
| visibility_off | Spectral Retrieval-Augmented Time-Series Forecasting | H. Nguyen, M. Nguyen, Dung Nguyen, Hung Le | 2026-06-17 | ArXiv | 0 | 3 |
| visibility_off | A Foundation Model-Assisted Observability Framework for Multimodal Anomaly Detection | Anastasios Zafeiropoulos, Gerasimos Mountakis, Grigorios Kakkavas, Ioannis Tzanettis, Alexandros-Panagiotis Stylos, Symeon Papavassiliou | 2026-06-01 | 2026 IEEE 27th International Conference on High Performance Switching and Routing (HPSR) | 0 | 12 |
| visibility_off | TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning | Fangxu Yu, Tao Feng, Dehai Min, Lu Cheng, Ge Liu, Tianyi Zhou | 2026-07-09 | ArXiv | 1 | 7 |
| visibility_off | Multivariate Time Series Forecasting needs Cross Variable Loss | Kuiye Ding, Yifan Hu, Hanchen Wang, Hao Xue | 2026-08-06 | ArXiv | 0 | 4 |
| visibility_off | TFT-GCN: A Time-Frequency Based Model for Time Series Anomaly Detection | Zhenchang Xia, Xusheng Xu, Libing Wu, Bingyi Liu, Long Yuan, Bolong Zheng | 2026-09-01 | IEEE Transactions on Knowledge and Data Engineering | 0 | 15 |
| visibility_off | ALER-TI: Aligned Latent Embedding Retrieval for Time Series Imputation | X. Truong, T. Lê, T. Kieu, Thi-Thu Nguyen, N. Nguyen | 2026-07-08 | ArXiv | 0 | 14 |
| visibility_off | Improving Coherence in Hierarchical Time Series Forecasting using Structured Temporal Fusion | Ruchi Pakhle | 2026-06-26 | ArXiv | 0 | 0 |
| visibility_off | Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection | M. Uray, S. Messineo, Roland Kwitt, Stefan Huber | 2026-07-14 | ArXiv | 0 | 5 |
| visibility_off | Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches | L. Fertig, Lukas Kirchdorfer, Tobias Sesterhenn | 2026-07-30 | ArXiv | 0 | 3 |
| visibility_off | MSCENet: A Multi-Scale Correlation Enhanced Network for Anomaly Detection | Long Zhao, Shixun Ji, Zhipeng Wang, Bin Cheng, Bin He | 2026-07-07 | ArXiv | 0 | 4 |
| visibility_off | Anomaly Detection in Multivariate Industrial Signals: LLMs, TSFMs, or Classical Deep Learning | Allen Baranov, Sarah Alnegheimish, Alfredo Cuesta-Infante, Weizhong Yan, M. Abbaszadeh, K. Veeramachaneni | 2026-07-03 | PHM Society European Conference | 0 | 38 |
| visibility_off | TA-SparseMG: Trend-Aware Sparse Forecasting via Multi-Scale Gating for Long-Term Time Series | Wenchao Liu, Hongbing Wang, Youji Zhu, Xiaodong Liu, Xiang-guang Xiong | 2026-06-26 | ArXiv | 0 | 9 |
| visibility_off | Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting | Xingsheng Chen, Deyu Yi, S. Yiu | 2026-07-07 | ArXiv | 0 | 8 |
| Abstract | Title | Authors | Publication Date | Journal/Conference | Citation count | Highest h-index |