Summary of Forecasting Four Business Cycle Phases Using Machine Learning: a Case Study Of Us and Eurozone, by Elvys Linhares Pontes et al.
Forecasting Four Business Cycle Phases Using Machine Learning: A Case Study of US and EuroZone
by Elvys Linhares Pontes, Mohamed Benjannet, Raymond Yung
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Machine learning models have shown promise in automatically analyzing the state of the economy, enabling more accurate forecasting of business phases (expansion, slowdown, recession, and recovery) in the United States and EuroZone. The study compared three machine learning approaches to classify business cycle phases, with Multinomial Logistic Regression (MLR) achieving the best results. MLR achieved an accuracy of 65.25% (Top1) and 84.74% (Top2) for the EuroZone and 75% (Top1) and 92.14% (Top2) for the United States, demonstrating its potential in predicting business cycles accurately. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can help predict when an economy is growing or shrinking, which is important for making good decisions about money and businesses. Researchers compared different machine learning methods to see how well they could classify economic phases like expansion, slowdown, recession, and recovery. The best method was Multinomial Logistic Regression (MLR), which did a great job of predicting these phases in both the US and EuroZone. |
Keywords
* Artificial intelligence * Logistic regression * Machine learning
