Summary of Quantile Deep Learning Models For Multi-step Ahead Time Series Prediction, by Jimmy Cheung et al.
Quantile deep learning models for multi-step ahead time series prediction
by Jimmy Cheung, Smruthi Rangarajan, Amelia Maddocks, Xizhe Chen, Rohitash Chandra
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Statistical Finance (q-fin.ST); Methodology (stat.ME)
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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 framework integrates quantile regression with deep learning for multi-step time series prediction, enhancing predictive capabilities by providing a nuanced understanding of values. The novel approach elevates deep learning models by incorporating quantile regression, enabling more accurate predictions under high volatility and extreme conditions. The framework is tested on two cryptocurrencies (Bitcoin and Ethereum) using daily close-price data and benchmark datasets, outperforming conventional deep learning models in terms of uncertainty quantification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict future values in time series data uses a combination of deep learning and a statistical method called quantile regression. This helps us understand the range of possible outcomes and provides more accurate predictions when things get really volatile. The team tested this approach on two popular cryptocurrencies, Bitcoin and Ethereum, using daily price data and compared it to other methods from the literature. They found that their new approach can give better predictions and provide more information about uncertainty. |
Keywords
» Artificial intelligence » Deep learning » Regression » Time series
