東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Time series forecasting using machine learning = case studies with R and iForecast /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Time series forecasting using machine learning/ by Tsung-wu Ho.
Reminder of title:	case studies with R and iForecast /
Author:	Ho, Tsung-wu.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	ix, 131 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Economic forecasting - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-3-031-97946-0
ISBN:	9783031979460

Time series forecasting using machine learning = case studies with R and iForecast /
Ho, Tsung-wu.

Time series forecasting using machine learningcase studies with R and iForecast /[electronic resource] :by Tsung-wu Ho. - Cham :Springer Nature Switzerland :2025. - ix, 131 p. :ill. (chiefly col.), digital ;24 cm.

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.

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.

ISBN: 9783031979460

Standard No.: 10.1007/978-3-031-97946-0doiSubjects--Topical Terms:

646553
Economic forecasting
--Mathematical models.

LC Class. No.: HB3730

Dewey Class. No.: 330.0112

Time series forecasting using machine learning = case studies with R and iForecast /
LDR:02279nmm a2200349 a 4500 001 2414385
003 DE-He213
005 20250831130213.0
006 m d
007 cr nn 008maaau
008 260205s2025 sz s 0 eng d
020 $a 9783031979460 $q (electronic bk.)
020 $a 9783031979453 $q (paper)
024 7 $a 10.1007/978-3-031-97946-0 $2 doi
035 $a 978-3-031-97946-0
040 $a GP $c GP
041 0 $a eng
050 4 $a HB3730
072 7 $a PBT $2 bicssc
072 7 $a K $2 bicssc
072 7 $a BUS061000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a K $2 thema
082 0 4 $a 330.0112 $2 23
090 $a HB3730 $b .H678 2025
100 1 $a Ho, Tsung-wu. $3 3184600
245 1 0 $a Time series forecasting using machine learning $h [electronic resource] : $b case studies with R and iForecast / $c by Tsung-wu Ho.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a ix, 131 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Economic forecasting $x Mathematical models. $3 646553
650 0 $a Time-series analysis. $3 532530
650 0 $a Machine learning. $3 533906
650 1 4 $a Statistics in Business, Management, Economics, Finance, Insurance. $3 3538572
650 2 4 $a Time Series Analysis. $3 3538821
650 2 4 $a Statistical Software. $3 3596845
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-97946-0
950 $a Mathematics and Statistics (SpringerNature-11649)