R语言机器学习学术应用

现场

3000元/2600元(学生优惠价仅限全日制本科及硕士在读)

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3000元/2600元(学生优惠价仅限全日制本科及硕士在读)
上课地点:远程直播,提供录播回放 讲师:何宗武 报名时间:2020-8-10 - 2020-8-12 开课时间:2020年8月10-12日(三天)

本课程针对「如何执行机器学习的时间序列预测」,关键领域是经济与财务实证上的预测性(Predictability),议题包括,资产报酬率和汇率变动的预测性,失业率预测,波动预测(volatility forecasting)以及机器学习之因果检测(Machine learning Granger causality)。这些议题在财经学术文献上皆有一定的地位,若能以机器学习的方法提出贡献,研究成果必受肯定。

 

本课程实做部份,皆会以一份期刊文献关键论文(key paper)为基准,以程序代码说明与对照实证文献上的重点,然后针对机器学习的可延伸贡献出做出对照结果;更重要的是,课程会针对特定议题,提供word的英文写作模板,学习者研习之后,将可以很快上手,完成一篇以国际期刊为目标的研究大纲。

 

讲师介绍:

何宗武教授,美国University of Utah经济学博士,现为台湾师范大学管理学院教授。专长为资产订价,总体计量,和融合经济计量方法和机器学习的计量数据科学 (Econometric Data Science)。何教授曾在同济大学经济与金融学院担任暑期客座高等计量方法讲席,2019年将多年讲稿由机械工业出版为经济与金融计量方法,综合著作有7本计量经济和大数据解析的专书。

有近30篇发布在优良的国际期刊,如J. of Applied Statistics, JIMF, JIFM, Empirical Economics, J. of Macroeconomics等,最近一篇以机器学习方法设计投资组合,已被Journal of Financial Data Science接受,将于2020年6月刊登,此文提出改善投资组合的鸡尾酒算法,在SSRN下载量名列前10%.

 

课程大纲:


天数

内容

时间序列预测之统计学习篇

1

Theory: Features of time series data and forecasting basics

R Lab: time series objects (libraries of timeSeries, xts, & mFilters)

2

Statistical Learning (SL):

(0.5 Hour) One-step forecasting: one-step ahead model fit

(0.5 Hour) Multi-step forecasting: recursive and direct methods

(6 Hours) Linear models: ARIMAs, ETS, BATS, GAMS, Bagged; 案例实做与写作范例

(5 hours) Nonlinear models: Neural Network, Smooth Transition, and AAR; 案例实做与写作范例

R Lab: libraries of forecast, tyDyn, vars, and MSVAR.

Research Issues: unemployment forecasting, predictability of exchange rates and asset returns.

参考文献:

1. Ahmed, N. K., Atiya, A. F., Gayar, N. E., & El-Shishiny, H. (2010). An empirical comparison of machine learning models for time series forecasting. Econometric Reviews, 29(5-6), 594-621.

2. Bergmeir, C., Hyndman, R. J., & Benítez, J. M. (2016). Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation. International Journal of Forecasting, 32(2), 303-312.

a) Chakraborty, C., & Joseph, A. (2017). Machine learning at central banks. Staff working paper No. 674, Bank of England.

3. Chevillon, G (2007) Direct multi-step estimation and forecasting. Journal of Economic Surveys, 21(4), pp. 746–785.

4. Clements, M and D Hendry (1998). Forecasting Economic Time Series. Forecasting economic time series. Cambridge University Press.

5. Cochrane John H. (2008) The Dog That Did Not Bark: A Defense of Return Predictability. Review of Financial Studies, 21(4), 1532-1575.

6. De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496), 1513-1527.

7. De Stefani, J., Caelen, O., Hattab, D., & Bontempi, G. (2017). Machine learning for multi-step ahead forecasting of volatility proxies. In MIDAS@ PKDD/ECML,17-28.

8. Edlund Per-Olov and Karlsson S. (1993) Forecasting the Swedish Unemployment Rate: VAR us. Transfer Function Modelling. International Journal of Forecasting, 9(1), pp. 61-76.

9. Feng, L., & Zhang, J. (2014). Application of artificial neural networks in tendency forecasting of economic growth. Economic Modelling, 40, 76-80.

10. Franses, P. H., & Van Dijk, D. (2000). Non-linear Time Series Models in Empirical Finance, Cambridge University Press.

11. Gogas, P., Papadimitriou, T., Matthaiou, M., & Chrysanthidou, E. (2015). Yield curve and recession forecasting in a machine learning framework. Computational Economics, 45(4), 635-645.

12. Hafezi, R., Shahrabi, J., & Hadavandi, E. (2015). A BAT-Neutral network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price. Applied Soft Computing, 29, 196-210.

13. Hall Aaron Smalter (2018) Machine Learning Approaches to Macroeconomic Forecasting. Economic Review, Federal Reserve Bank of Kansas, Available at: https://www.kansascityfed.org/~/media/files/publicat/econrev/econrevarchive/2018/4q18smalterhall.pdf 

14. Hamzaçebi, C., Akay, D., & Kutay, F. (2009). Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting. Expert Systems with Applications, 36(2), 3839-3844.

15. Ho, Tsung-Wu (2019) Machine Learning is not as Good as you Think for Time Series Forecasting: Evidence from Multistep Forecasting. Available at: https://ssrn.com/abstract=3496138

16. Ho, Tsung-Wu (2020) Portfolio Selection using portfolio committees. Forthcoming in the Journal of Financial Data Science.

17. Hyndman, R. J. and Athanasopoulos, G. (2018). Forecasting: principles and practice. 2nd edition, OTexts.com/fpp2.

18. Lewellen Jonathan (2004) Predicting returns with financial ratios. Journal of Financial Economics, 74, 209–235.

19. Montgomery, Alan L, Victo Zarnowitz, Ruey S. Tsay, and George C. Tiao (1998) Forecasting the U.S. Unemployment Rate. Journal of the American Statistical Association, 93(442), pp. 478-493.

20. Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4), 2162-2172.

21. Rothman Philip (1998) Forecasting Asymmetric Unemployment rates. Review of Economics and Statistics, 80(1), pp.164-168.

22. Saad, E., Prokhorov, D., and Wunsch, D. (1998) Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on Neural Networks 9(6), pp. 1456–1470.

23. Serinaldi, F. (2011). Distributional modeling and short-term forecasting of electricity prices by generalized additive models for location, scale and shape. Energy Economics, 33(6), 1216-1226.

24. Sorjamaa, A, J Hao, N Reyhani, Y Ji, and A Lendasse (2007). Methodology for long-term prediction of time series. Neurocomputing,70(16), pp. 2861–2869.

25. Tasci, Murat (2012) Ins and Outs of Unemployment in the Long-Run: Unemployment Flows and the Natural Rate. Working Paper #12-24, Federal Reserve Bank of Cleveland.

26. Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research, 54(8), 799-805.

27. Torres, J. F., Galicia, A., Troncoso, A., & Martínez-Álvarez, F. (2018). A scalable approach based on deep learning for big data time series forecasting. Integrated Computer-Aided Engineering, 25(4), 335-348.

28. Ülke, V., Sahin, A., & Subasi, A. (2018). A comparison of time series and machine learning models for inflation forecasting: empirical evidence from the USA. Neural Computing and Applications, 30(5), 1519-1527.

29. Vapnik V. (2000) The Nature of Statistical Learning Theory, 2nd. Springer-Verlag, New York. 

30. Wood, S. N., & Augustin, N. H. (2002). GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecological modelling, 157(2-3), 157-177.

31. Zhang, G., & Hu, M. Y. (1998). Neural network forecasting of the British pound/US dollar exchange rate. Omega, 26(4), 495-506.

32. Zhao, Y., Li, J., & Yu, L. (2017b). A deep learning ensemble approach for crude oil price forecasting. Energy Economics, 66, 9-16.

33. Zhao, Z., Chen, W., Wu, X., Chen, P. C., & Liu, J. (2017a). LSTM network: a deep learning approach for short-term traffic forecast. IET Intelligent Transport Systems, 11(2), 68-75.

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