Summary of Hierarchical Classification Auxiliary Network For Time Series Forecasting, by Yanru Sun et al.
Hierarchical Classification Auxiliary Network for Time Series Forecasting
by Yanru Sun, Zongxia Xie, Dongyue Chen, Emadeldeen Eldele, Qinghua Hu
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper introduces a novel approach to time series forecasting, addressing the issue of over-smooth predictions in deep learning models trained with Mean Square Error (MSE) loss. By tokenizing time series values and training forecasting models via cross-entropy loss, the authors aim to capture high-entropy features from complex and unpredictable time series data. The Hierarchical Classification Auxiliary Network (HCAN) is a general model-agnostic component that integrates multi-granularity high-entropy features at different hierarchy levels, mitigating over-confidence in softmax loss via evidence theory. The paper also proposes a Hierarchical Consistency Loss to maintain prediction consistency across hierarchy levels. Extensive experiments integrating HCAN with state-of-the-art forecasting models demonstrate substantial improvements over baselines on several real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to forecast what will happen in the future based on past data. Right now, computers have trouble predicting things that are really hard to predict because they make their predictions too smooth and don’t take into account all the little details. The authors came up with an idea to break down the data into smaller chunks and train a computer model to predict each chunk separately. This helps the computer learn from all the different parts of the data, rather than just averaging it out. They also developed a special tool that helps the computer be more confident in its predictions by taking into account how certain it is about each one. |
Keywords
» Artificial intelligence » Classification » Cross entropy » Deep learning » Mse » Softmax » Time series
