The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Functional Forecasting of Seasonality

계절변동의 함수적 예측

  • Lee, Geung-Hee (Department of Information Statistics, Korea National Open University)
  • 이긍희 (한국방송통신대학교 정보통계학과)
  • Received : 2015.05.18
  • Accepted : 2015.06.24
  • Published : 2015.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

It is important to improve the forecasting accuracy of one-year-ahead seasonal factors in order to produce seasonally adjusted series of the following year. In this paper, seasonal factors of 8 monthly Korean economic time series are examined and forecast based on the functional principal component regression. One-year-ahead forecasts of seasonal factors from the functional principal component regression are compared with other forecasting methods based on mean absolute error (MAE) and mean absolute percentage error (MAPE). Forecasting seasonal factors via the functional principal component regression performs better than other comparable methods.

통계청과 한국은행 등 통계작성기관에서 이용되고 있는 계절조정은 연간 경제통계 작성시 시계열을 예측한 후 계절조정방법을 적용하여 1년 후 계절변동을 예측하고 원통계 작성시 원통계에서 이를 제거하여 계절조정계열을 작성하고 있다. 이 경우 계절변동을 효과적으로 예측하는 것이 계절조정계열의 품질 향상을 위해 무엇보다 중요하다. 계절변동은 1년 단위로 비슷한 함수적 형태를 지니면서 변하므로 계절변동은 일종의 함수적 시계열이다. 함수적 시계열은 함수적 주성분분석을 바탕으로 한 함수적 시계열모형으로 예측할 수 있다. 본 연구에서는 함수적 시계열 모형을 이용하여 향후 1년간 계절변동을 예측하는 방안을 마련하고 X-11 방식 등 기존의 예측방법과 비교하여 유용성을 파악하였다.

Keywords

References

  1. Bloem, A. M., Dippelsman, R. J. and Maehle, N. O. (2001). Quarterly National Accounts Manual-Concepts, Data Sources, and Compilation, International Monetary Fund.
  2. Hyndman, R. J. and Shang, H. L. (2009). Forecasting functional time series (with discussion), Journal of the Korean Statistical Society, 38, 199-221. https://doi.org/10.1016/j.jkss.2009.06.002
  3. Hyndman, R. J. and Shang, H. L. (2015). Package ftsa, http://cran.r-project.org/web/packages/ftsa/
  4. Hyndman, R. J. and Ullah, M. S. (2007). Robust forecasting of mortality and fertility rates: A functional data approach, Computational Statistics and Data Analysis, 51, 4942-4956. https://doi.org/10.1016/j.csda.2006.07.028
  5. Lee, G.-H. (1998). X-12 ARIMA seasonal adjustment in Korean economic time series, Economic Analysis, 4, 205-242.
  6. Lee, G.-H. and Lee, H. (2013). Seasonal adjustment in Korean economic time series with X-13ARIMA-SEATS, Quarterly National Accounts Review, 4, 205-242.
  7. McKenzie, S. K. (1984). Concurrent seasonal adjustment with census X-11, Journal of Business and Economic Statistics, 2, 235-249.
  8. Ramsay, J. O. and Silverman, B. W. (2005). Functional Data Analysis, 2nd ed, Springer, New York.
  9. Shang, H. L. (2011). A survey of functional principal component analysis, Working Paper 06/11, Monash University.
  10. Shang, H. L. (2012). Point and interval forecasts of age-specific fertility rates: A comparison of functional principal component methods, Journal of Population Research, 29, 249-267. https://doi.org/10.1007/s12546-012-9087-4
  11. Shang, H. L. (2013). Functional time series approach for forecasting very short-term electricity demand, Journal of Applied Statistics, 40, 152-168. https://doi.org/10.1080/02664763.2012.740619
  12. U.S. Census Bureau (2012). X-13ARIMA-SEATS Reference Manual, Statistical Research Division, U.S. Census Bureau.