Summary of Multi-scale Dilated Convolution Network For Long-term Time Series Forecasting, by Feifei Li et al.
Multi-Scale Dilated Convolution Network for Long-Term Time Series Forecasting
by Feifei Li, Suhan Guo, Feng Han, Jian Zhao, Furao Shen
First submitted to arxiv on: 9 May 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 proposes a novel method called Multi Scale Dilated Convolution Network (MSDCN) for accurate long-term time series forecasting. By utilizing a shallow dilated convolution architecture, MSDCN captures the period and trend characteristics of long time series data, outperforming previous state-of-the-art approaches. The approach combines traditional autoregressive models with exponentially growing dilations and varying kernel sizes to sample time series data at different scales. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us better predict what will happen in the future by analyzing big patterns in data over a long period of time. This is important for making decisions and planning ahead. The authors developed a new way to analyze this kind of data using something called dilated convolutions, which allows them to find patterns at different scales. They tested their approach on eight real-world datasets and found that it worked better than other methods. |
Keywords
» Artificial intelligence » Autoregressive » Time series
