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...
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| প্রধান লেখক: | |
|---|---|
| সংস্থা লেখক: | |
| বিন্যাস: | বৈদ্যুতিক বৈদ্যুতিন গ্রন্থ |
| ভাষা: | ইংরেজি |
| প্রকাশিত: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
|
| সংস্করন: | 1st ed. 2025. |
| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | https://doi.org/10.1007/978-3-031-97946-0 |
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| 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. |
| 250 | |a 1st ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2025. | |
| 300 | |a 1 online resource (IX, 131 p. 89 illus.). |b 72 illus. in color. | ||
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| 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. Please note that a more accessible version of this eBook is available as ePub. | |
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