Summary of Efficanet: Efficient Time Series Forecasting with Convolutional Attention, by Xinxing Zhou et al.
EffiCANet: Efficient Time Series Forecasting with Convolutional Attention
by Xinxing Zhou, Jiaqi Ye, Shubao Zhao, Ming Jin, Chengyi Yang, Yanlong Wen, Xiaojie Yuan
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| 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 proposed EffiCANet model is an Efficient Convolutional Attention Network designed for multivariate time series forecasting in domains like industrial monitoring and smart cities. The model aims to capture long-range dependencies and complex inter-variable relationships while maintaining computational efficiency. It consists of three key components: the Temporal Large-kernel Decomposed Convolution (TLDC) module, Inter-Variable Group Convolution (IVGC) module, and Global Temporal-Variable Attention (GTVA) mechanism. The TLDC module reduces computational overhead by decomposing large kernels into smaller ones. The IVGC module captures complex relationships among variables. The GTVA mechanism prioritizes critical features. Evaluations across nine benchmark datasets show that EffiCANet achieves the maximum reduction of 10.02% in MAE over state-of-the-art models, while cutting computational costs by 26.2%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new model called EffiCANet to predict future values from sensor data. The model is good at catching patterns that happen over long periods of time and can handle complex relationships between different variables. It’s also efficient, which means it uses less computer power than other models. The model has three parts: one for capturing long-term patterns, one for handling complex variable relationships, and one for focusing on the most important features. The paper tested the model on many datasets and showed that it performs better than other state-of-the-art models while using less computing resources. |
Keywords
» Artificial intelligence » Attention » Mae » Time series
