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This book uses R package, iForecast, to conduct financial economic time series forecasting with machine learning methods, especially the generation of dynamic forecasts out-of-sample. Machine learning methods cover enet, random forecast, gbm, and autoML etc., including binary economic time series. The book explains the problem about the generation of recursive forecasts in machine learning framework, under which, there are no covariates, namely, input (independent) variables. This case is pretty common in real decision environment, for example, the decision-making wants 6-month forecasts in the real future, under which there are no covariates available; therefore, practitioners use recursive or multistep, forecasts. Besides macro-econometric modelling which uses VAR (vector autoregression) to overcome the problem of multivariate regression, this book offers a Machine-Learning VAR routine, which is found to improve the performance of multistep forecasting.
List of contents
Preface.- Chapter 1 Time Series Basics in R.- Chapter 2 Predictive Time Series Modelling.- Chapter 3 Forecasting using Machine Learning Methods.- Chapter 4 Special Topics.- Chapter 5 Predictive Case Studies Training by Rolling.- References.
About the author
Tsung-wu Ho is a professor at National Taiwan Normal University. His research interests are Asset Pricing, Machine Learning, Economic and Decision Making.
Summary
This book uses R package, iForecast, to conduct financial economic time series forecasting with machine learning methods, especially the generation of dynamic forecasts out-of-sample. Machine learning methods cover enet, random forecast, gbm, and autoML etc., including binary economic time series. The book explains the problem about the generation of recursive forecasts in machine learning framework, under which, there are no covariates, namely, input (independent) variables. This case is pretty common in real decision environment, for example, the decision-making wants 6-month forecasts in the real future, under which there are no covariates available; therefore, practitioners use recursive or multistep, forecasts. Besides macro-econometric modelling which uses VAR (vector autoregression) to overcome the problem of multivariate regression, this book offers a Machine-Learning VAR routine, which is found to improve the performance of multistep forecasting.
