Summary of A Comprehensive Survey Of Time Series Forecasting: Architectural Diversity and Open Challenges, by Jongseon Kim (1 and 3) et al.
A Comprehensive Survey of Time Series Forecasting: Architectural Diversity and Open Challenges
by Jongseon Kim, Hyungjoon Kim, HyunGi Kim, Dongjun Lee, Sungroh Yoon
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper surveys recent advancements in time series forecasting, focusing on architectural diversification and its impact on performance. It highlights how fundamental deep learning architectures like MLPs, CNNs, RNNs, and GNNs have been applied to solve time series forecasting problems, but are limited by their structural biases. Transformer models, known for handling long-term dependencies, excel in this domain. However, recent findings show that simple linear layers can outperform Transformers, opening up opportunities for diverse architectures. The paper provides a historical context, analysis of architectural diversification, and discusses emerging trends like hybrid, diffusion, Mamba, and foundation models. It also addresses open challenges like channel dependency, distribution shift, causality, and feature extraction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series forecasting is important for making decisions in various fields. Researchers have been developing different deep learning models to help with this task. Some models are good at handling long-term dependencies, but others can still do well even without these special abilities. This paper looks back on the history of time series forecasting and explores how different models work together. It also talks about new ideas like combining different models or using simple layers instead of complex ones. |
Keywords
» Artificial intelligence » Deep learning » Diffusion » Feature extraction » Time series » Transformer
