Are Transformers Effective for Time Series Forecasting?

2022/05/26 by Ailing Zeng, Zeng, Ailing, Muxi Chen +5 · 1 voice · 392 citations
Computer Science · #Advanced Text Analysis Techniques #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Time Series Analysis and Forecasting #cs.AI #cs.LG

paper · pdf · doi:10.48550/arxiv.2205.13504

Code is available at https://github.com/cure-lab/LTSF-Linear

openalex publication_date 2022/05/26 · arxiv published 2022/05/26 · arxiv created 2022/08/17 · arxiv updated 2022/08/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence. However, in time series modeling, we are to extract the temporal relations in an ordered set of continuous points. While employing positional encoding and using tokens to embed sub-series in Transformers facilitate preserving some ordering information, the nature of the permutation-invariant self-attention mechanism inevitably results in temporal information loss. To validate our claim, we introduce a set of embarrassingly simple one-layer linear models named LTSF-Linear for comparison. Experimental results on nine real-life datasets show that LTSF-Linear surprisingly outperforms existing sophisticated Transformer-based LTSF models in all cases, and often by a large margin. Moreover, we conduct comprehensive empirical studies to explore the impacts of various design elements of LTSF models on their temporal relation extraction capability. We hope this surprising finding opens up new research directions for the LTSF task. We also advocate revisiting the validity of Transformer-based solutions for other time series analysis tasks (e.g., anomaly detection) in the future. Code is available at: \urlhttps://github.com/cure-lab/LTSF-Linear.

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• OrEdge: Efficient Multi-Modal Anomaly Detection in Distributed Software Systems via Orthogonal-Domain Learning
• An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Book
• SPAT: Sensitivity-based Multihead-attention Pruning on Time Series Forecasting Models
• Evaluating Large Language Models for Real-World Engineering Tasks
• A Comparative Study of Transformer-Based Models for Multi-Horizon Blood Glucose Prediction
• OLinear: A Linear Model for Time Series Forecasting in Orthogonally Transformed Domain
• Non-Stationary Time Series Forecasting Based on Fourier Analysis and Cross Attention Mechanism
• Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering
• CASA: CNN Autoencoder-based Score Attention for Efficient Multivariate Long-term Time-series Forecasting
• Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillation
• How Effective are Large Time Series Models in Hydrology? A Study on Water Level Forecasting in Everglades
• Dual-Forecaster: A Multimodal Time Series Model Integrating Descriptive and Predictive Texts
• Unlocking the Potential of Linear Networks for Irregular Multivariate Time Series Forecasting
• Deep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performance
• Visual Reasoning over Time Series via Multi-Agent System
• Gateformer: Advancing Multivariate Time Series Forecasting through Temporal and Variate-Wise Attention with Gated Representations
• Report the Floor: A Training-Free Conformal Interval Is a Mandatory Baseline for Probabilistic Time-Series Forecasting
• NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting
• TopoPrimer: The Missing Topological Context in Forecasting Models
• Can Transformers predict system collapse in dynamical systems?
• Multimodal Conditioned Diffusive Time Series Forecasting
• TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models
• Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics
• TSRM: A Lightweight Temporal Feature Encoding Architecture for Time Series Forecasting and Imputation
• Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity
• Smart Predict--then--Optimize Paradigm for Portfolio Optimization in Real Markets
• Tokenizing Stock Prices for Enhanced Multi-Step Forecast and Prediction
• CANet: ChronoAdaptive Network for Enhanced Long-Term Time Series Forecasting under Non-Stationarity
• Goal-Oriented Time-Series Forecasting: Foundation Framework Design
• iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network
• RDPA: Dual-domain deep learning for robust ADS-B spoofing detection via reversible mormalization and spectral patch attention
• Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning
• Energy-Aware Pattern Disentanglement: A Generalizable Pattern Assisted Architecture for Multi-task Time Series Analysis
• Foundation Models for Time Series: A Survey
• Multivariate Time Series Forecasting needs Cross Variable Loss
• Local-Global Feature Mixer and Trend-Guided Consistent Learning for Remaining Useful Life Prediction of Rotating Machinery
• FISformer: Replacing Self-Attention with a Fuzzy Inference System in Transformer Models for Time Series Forecasting
• Air Quality Prediction with A Meteorology-Guided Modality-Decoupled Spatio-Temporal Network
• Leveraging Large Self-Supervised Time-Series Models for Transferable Diagnosis in Cross-Aircraft Type Bleed Air System
• ms-Mamba: Multi-scale Mamba for Time-Series Forecasting
• Enhancing Time Series Forecasting via Multi-Level Text Alignment with LLMs

Discussions

• Are Transformers Effective for Time Series Forecasting? [hn, 3 points, 0 comments]

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