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23.9 MB
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51590CEBAE9508978C5AC725037281949179F318
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Sept. 14, 2025, 11:54 a.m.
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(Last updated: Sept. 14, 2025, 11:56 a.m.)
| File | Size |
|---|---|
| Tsung H. Time Series Forecasting using Machine Learning...with R..iForecast 2025.pdf | 23.9 MB |
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2025-09-14
| Uploaded by andryold1 | Size 23.9 MB | Health [ 60 /25 ] | Added 2025-09-14 |
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| Uploaded by RedirkraDehT | Size 698.3 MB | Health [ 0 /4 ] | Added 2023-10-28 |
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SOURCE: Tsung H. Time Series Forecasting using Machine Learning...with R..iForecast 2025
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MEDIAINFO
Textbook in PDF format 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. Machine Learning methods are the subject of studies by predictive practitioners but from the perspective of R, the use of Machine Learning for time series forecasting is a relatively new field. One of the demands, as policy expects, is the need to forecast the real future, and not the back-testing future or a pseudo future that uses cross-validation/training data. While building the model, ACF-type lags may also have to be trained, instead of PACF. To this end, an R package iForecast is developed that features many Machine Learning methods, such as SVM, random forest, GBM, elastic net, xgboost and the AutoML routines, and flexible data inputs and lag specifications. In terms of applied econometrics, this text uses a wrapper to estimate VAR modelling using Machine Learning methods. This demonstrates the richness and versatility of modern Machine Learning applications in terms of time series forecasting. It is an introduction to time series forecasting using R, but it is not an introductory statistics/econometrics textbook. Readers should have a basic understanding of matrix algebra and data tables, and we also assumed that readers are familiar with serial dependency concepts, such as ACF/PACF and the implications of serial correlation. This book is intended as an instrumental text for a time series forecasting course, with an emphasis on the use of Machine Learning methods to forecast a time series future. It will also be of use to economists and econometricians who wish to use R to conduct time series forecasting: specifically, economic time series. Preface Time Series Basics in R Predictive Time Series Modeling Forecasting Using Machine Learning Methods Special Topics Predictive Case Studies: Training by Rolling
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