Summary of Financial Time-series Forecasting: Towards Synergizing Performance and Interpretability Within a Hybrid Machine Learning Approach, by Shun Liu et al.
Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach
by Shun Liu, Kexin Wu, Chufeng Jiang, Bin Huang, Danqing Ma
First submitted to arxiv on: 31 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistical Finance (q-fin.ST)
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 This paper presents a comparative study on hybrid machine learning algorithms for predicting Bitcoin prices, with a focus on enhancing model interpretability. The authors introduce several candidate models, including linear regression (OLS and LASSO), long-short term memory (LSTM), and decision tree regressors. Experimental results show that the linear regression model achieves the best performance among the candidate models. To improve model interpretability, the study also reviews preprocessing techniques for time-series statistics, such as decomposition, auto-correlational function, and exponential triple forecasting. These techniques aim to uncover latent relations and complex patterns in financial time-series data. The authors believe that this work may inspire further research in time-series analysis and its applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how to predict Bitcoin prices using different machine learning models. The researchers test several algorithms, including ones that use linear regression, long-short term memory, and decision trees. They find that one type of linear regression model performs the best. To make the models more understandable, the study also looks at ways to prepare financial data for analysis. These techniques help uncover hidden patterns in the data. The researchers hope that their work will lead to new discoveries in time-series analysis and its practical applications. |
Keywords
* Artificial intelligence * Decision tree * Linear regression * Lstm * Machine learning * Time series
