Review paper details long horizon forecasting challenges and deep learning solutions

A new review paper published on arXiv details the long horizon forecasting (LHF) problem in time series analysis, a challenge that has persisted for over 35 years. The paper explores how deep learning techniques, including various transforms, convolutional methods, and attention mechanisms, have been applied to address LHF. It also highlights data preprocessing and feature construction strategies that enhance performance, with specific attention to models like xLSTM and Triformer that show improved error propagation characteristics. AI

IMPACT Provides a comprehensive overview of deep learning techniques for long-term time series prediction, useful for researchers and practitioners.

RANK_REASON The item is a review paper on arXiv detailing a specific problem in time series analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• cs.LG
• deep learning
• ETTm2
• Long Horizon Forecasting
• time series analysis
• Triformer
• xLSTM

• paper
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