Image de couverture de livre

LATST: Are Transformers Necessarily Complex for Time-Series Forecasting

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Liang, Dizhen
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting
par: Xu, Xiongxiao, et autres
Publié: (2024)

VARMA-Enhanced Transformer for Time Series Forecasting
par: Song, Jiajun, et autres
Publié: (2025)

HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting
par: Wang, Tan, et autres
Publié: (2025)

SEDformer: Event-Synchronous Spiking Transformers for Irregular Telemetry Time Series Forecasting
par: Zhou, Ziyu, et autres
Publié: (2026)

Fredformer: Frequency Debiased Transformer for Time Series Forecasting
par: Piao, Xihao, et autres
Publié: (2024)

Unified Training of Universal Time Series Forecasting Transformers
par: Woo, Gerald, et autres
Publié: (2024)

CITRAS: Covariate-Informed Transformer for Time Series Forecasting
par: Yamaguchi, Yosuke, et autres
Publié: (2025)

TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
par: Wang, Yuxuan, et autres
Publié: (2024)

Ister: Linear Transformer for Efficient Multivariate Time Series Forecasting
par: Cao, Fanpu, et autres
Publié: (2024)

HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting
par: Feng, Shibo, et autres
Publié: (2025)

Why Do Transformers Fail to Forecast Time Series In-Context?
par: Zhou, Yufa, et autres
Publié: (2025)

Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting
par: Zhao, Yanjun, et autres
Publié: (2024)

Attention as Robust Representation for Time Series Forecasting
par: Niu, PeiSong, et autres
Publié: (2024)

FPN-fusion: Enhanced Linear Complexity Time Series Forecasting Model
par: Li, Chu, et autres
Publié: (2024)

TEAFormers: TEnsor-Augmented Transformers for Multi-Dimensional Time Series Forecasting
par: Kong, Linghang, et autres
Publié: (2024)

PSformer: Parameter-efficient Transformer with Segment Attention for Time Series Forecasting
par: Wang, Yanlong, et autres
Publié: (2024)

FreEformer: Frequency Enhanced Transformer for Multivariate Time Series Forecasting
par: Yue, Wenzhen, et autres
Publié: (2025)

iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network
par: Liang, Ziran, et autres
Publié: (2025)

The Forecast After the Forecast: A Post-Processing Shift in Time Series
par: Liang, Daojun, et autres
Publié: (2026)

Reasoning-Aware Training for Time Series Forecasting
par: Ahamed, Md Atik, et autres
Publié: (2026)

Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond
par: Ke, Yekun, et autres
Publié: (2024)

Deep Coupling Network For Multivariate Time Series Forecasting
par: Yi, Kun, et autres
Publié: (2024)

Time Series Forecastability Measures
par: Wang, Rui, et autres
Publié: (2025)

Ellipsoidal Time Series Forecasting
par: Wang, Qilin
Publié: (2025)

Performative Time-Series Forecasting
par: Zhao, Zhiyuan, et autres
Publié: (2023)

Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting
par: Wang, Muyao, et autres
Publié: (2024)

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting
par: Kiefer, Nicholas, et autres
Publié: (2024)

Dynamic Long-Term Time-Series Forecasting via Meta Transformer Networks
par: Ma'sum, Muhammad Anwar, et autres
Publié: (2024)

EVEREST: An Evidential, Tail-Aware Transformer for Rare-Event Time-Series Forecasting
par: Zilinskas, Antanas, et autres
Publié: (2026)

CAPS: Unifying Attention, Recurrence, and Alignment in Transformer-based Time Series Forecasting
par: Pati, Viresh, et autres
Publié: (2026)

TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare
par: Song, Ziyang, et autres
Publié: (2023)

Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting
par: Hu, Jiaxi, et autres
Publié: (2024)

TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecasting
par: Hu, Jiaxi, et autres
Publié: (2024)

LETS Forecast: Learning Embedology for Time Series Forecasting
par: Majeedi, Abrar, et autres
Publié: (2025)

Test Time Learning for Time Series Forecasting
par: Christou, Panayiotis, et autres
Publié: (2024)

VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting
par: Yang, Yingnan, et autres
Publié: (2024)

Leveraging 2D Information for Long-term Time Series Forecasting with Vanilla Transformers
par: Cheng, Xin, et autres
Publié: (2024)

Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting
par: Xu, Jingjing, et autres
Publié: (2024)

ReCycle: Fast and Efficient Long Time Series Forecasting with Residual Cyclic Transformers
par: Weyrauch, Arvid, et autres
Publié: (2024)

Patch-Level Tokenization with CNN Encoders and Attention for Improved Transformer Time-Series Forecasting
par: Nagrath, Saurish, et autres
Publié: (2026)