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The roles of differencing and dimension reduction in machine learning forecasting of employment level using the FRED big data

Title
The roles of differencing and dimension reduction in machine learning forecasting of employment level using the FRED big data
Authors
Choi J.-E.Shin D.W.
Ewha Authors
신동완
SCOPUS Author ID
신동완scopus
Issue Date
2019
Journal Title
Communications for Statistical Applications and Methods
ISSN
2287-7843JCR Link
Citation
Communications for Statistical Applications and Methods vol. 26, no. 5, pp. 497 - 506
Keywords
Deep neural networkDifferencingDimension reductionEmployment forecastGated recurrent unitLong short term memory
Publisher
Korean Statistical Society
Indexed
SCOPUS; KCI scopus
Document Type
Article
Abstract
Forecasting the U.S. employment level is made using machine learning methods of the artificial neural network: deep neural network, long short term memory (LSTM), gated recurrent unit (GRU). We consider the big data of the federal reserve economic data among which 105 important macroeconomic variables chosen by Mc- Cracken and Ng (Journal of Business and Economic Statistics, 34, 574-589, 2016) are considered as predictors. We investigate the influence of the two statistical issues of the dimension reduction and time series differencing on the machine learning forecast. An out-of-sample forecast comparison shows that (LSTM, GRU) with differencing performs better than the autoregressive model and the dimension reduction improves long-term forecasts and some short-term forecasts. © 2019 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved.
DOI
10.29220/CSAM.2019.26.5.497
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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