Springer Nature Switzerland Time Series Forecasting Using Machine Learning

Name: Springer Nature Switzerland Time Series Forecasting Using Machine Learning
Brand: Springer Nature Switzerland
Price: 117.69 EUR
Availability: InStock

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★ 4.9

117,69 €

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Preisalarm

★ 4.9

117,69 €

Bester Preis bei link.springer.com

Preisalarm

★ 4.9

117,69 €

Bester Preis bei link.springer.com

link.springer.com

117,69 €

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Produktinformationen

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.