Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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The roles of differencing and dimension reduction in machine learning forecasting of employment level using the FRED big data

  • Choi, Ji-Eun (Department of Statistics, Ewha Womans University) ;
  • Shin, Dong Wan (Department of Statistics, Ewha Womans University)
  • Received : 2019.06.27
  • Accepted : 2019.08.26
  • Published : 2019.09.30
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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 McCracken 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.

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References

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