Time Series Forecasting using Machine Learning Case Studies with R and iForecast /
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. T...
Gorde:
| Egile nagusia: | |
|---|---|
| Erakunde egilea: | |
| Formatua: | Baliabide elektronikoa eBook |
| Hizkuntza: | ingelesa |
| Argitaratua: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
|
| Edizioa: | 1st ed. 2025. |
| Gaiak: | |
| Sarrera elektronikoa: | https://doi.org/10.1007/978-3-031-97946-0 |
| Etiketak: |
Etiketa erantsi
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
|
| Gaia: | 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. |
|---|---|
| Deskribapen fisikoa: | 1 online resource (IX, 131 p. 89 illus.). 72 illus. in color. |
| ISBN: | 9783031979460 |
| DOI: | 10.1007/978-3-031-97946-0 |