Please use this identifier to cite or link to this item:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72920
Title: | 機器學習在商業週期的分類與預測之應用 Applications of Machine Learning in Classifying and Forecasting Business Cycles |
Authors: | Scott Schwartz 天空 |
Advisor: | 楊曙榮(Sunny Yang) |
Keyword: | 機器學習,商業週期,景氣衰退,預測,分類, Machine Learning,Forecasting,Recession,Business Cycle,Prediction,Classification, |
Publication Year : | 2019 |
Degree: | 碩士 |
Abstract: | The term “recession”is not only a sensitive topic to the workers, investors, and businesses that suffer immense loss during this time, but also economists whom continually struggle to predict them. Through the rise of Big Data, Machine Learning potentially gives statisticians and economists alike a new tool for predicting recessions. In order to explore this field, this paper asks two fundamental questions:
• Does machine learning help classify and forecast recessions within the business cycle? • Which models are most effective in predicting and classifying recessions? Applying the most common machine learning classification algorithms, we perform out-of-sample performance evaluations using a self-selected sample of macroeconomic indicators. Our findings imply that Random Forest, KNN, and Support Vector Machine models best classify and predict recessions. The analysis results suggest that machine learning has incredible potential in improving prediction accuracy. However, we believe these models can be further developed through additional research and application within the Deep Learning field of machine learning. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72920 |
DOI: | 10.6342/NTU201901681 |
Fulltext Rights: | 有償授權 |
Appears in Collections: | 管理學院企業管理專班(Global MBA) |
Files in This Item:
File | Size | Format | |
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ntu-108-1.pdf Restricted Access | 3.85 MB | Adobe PDF |
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