課程大綱： R語言機器學習學術應用培訓
R語言機器學習學術應用
基礎
Theory: Features of time series data and forecasting basics
R Lab: time series objects (libraries of timeSeries, xts, & mFilters)
中級
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; 案例實做與寫作范例
(5 hours) Nonlinear models: Neural Network, Smooth Transition, and AAR; 案例實做與寫作范例
R Lab: libraries of forecast, tyDyn, vars, and MSVAR.
Research Issues: unemployment forecasting, predictability of exchange rates and asset returns.
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