응용통계연구 (The Korean Journal of Applied Statistics)

  • 제20권1호
  • /
  • Pages.91-102
  • /
  • 2007
  • /
  • 1225-066X(pISSN)
  • /
  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지
DOI QR코드

DOI QR Code

공간시계열 자료에 대한 STARMA 모형과 STBL 모형의 예측력 비교

A Comparison on Forecasting Performance of STARMA and STBL Models with Application to Mumps Data

  • 이성덕 (충북대학교 정보통계학과, 기초과학연구소) ;
  • 이응준 (성균관대학교 통계학과, 대학원) ;
  • 박용석 (성균관대학교 통계학과, 대학원) ;
  • 주재선 (성균관대학교 통계학과, 대학원) ;
  • 이건명 (충북대학교 전기전자컴퓨터학부)
  • Lee, S.D. (Department of Computer Science Graduate School, Chungbuk National University) ;
  • Lee, Y.J. (Department of Statistics, Sungkyunkwan University) ;
  • Park, Y.S. (Department of Statistics, Sungkyunkwan University) ;
  • Joo, J.S. (Department of Statistics, Sungkyunkwan University) ;
  • Lee, K.M. (School of Electronics & Computer Science, Cuungbuk National University)
  • 발행 : 2007.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문은 공간시계열 자기회귀 이동평균(STARMA) 모형과 공간 시계열 중선형(STBL) 모형에 대해 식별, 추정, 예측 등의 통계적 절차와 특징들을 논하고, 두 모형을 비교하는데 목적이 있다. 사례 연구를 위 해 2001년부터 2006년까지 8개 지역으로부터 보고된 월별 Mumps 자료를 사용했고, 예측오차제곱합(SSF)을 활용하여 두 모형의 적합도를 비교하였다.

The major purpose of this article is to formulate a class of Space Time Autoregressive Moving Average(STARMA) model and Space Time Bilinear model(STBL), to discuss some of the their statistical properties such as model, identification approaches, some procedure for estimation and the predictions, and to compare the STARMA model with the STBL model. For illustration, The Mumps data reported from eight city & provinces monthly over the years 2001-2006 are used and the result from STARMA and STBL model are compared with using SSF(Sum of Square Prediction Error).

키워드

참고문헌

  1. 김선우, 정애란, 이성덕 (2005). 공간자료에 대한 지리적 가중회귀 모형과 크리깅의 비교, <응용통계 연구>, 18, 271-280 https://doi.org/10.5351/KJAS.2005.18.2.271
  2. Anderson, T. W. (1984). An Introduction to Multivariate Statistical Analysis, 2nd ed., John Wiley & Sons, New York, 1-24
  3. Billard, L. and Dai, Y. (2003). Maximum likelihood estimation in space time bilinear models, Journal of Time Series Analysis, 24, 25-44 https://doi.org/10.1111/1467-9892.00291
  4. Billard, L. and Dai, Y. (2003). Modeling spatial-temporal epidemics, Department of Statistic, 1-34
  5. Dai, Y. and Billard, L. (1998). A space-time bilinear model and its identification, Journal of Time Series Analysis, 19, 657-679 https://doi.org/10.1111/1467-9892.00115
  6. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems, Transactions of ASME-Journal of Basic Engimeering, Ser. D, 82, 35-45 https://doi.org/10.1115/1.3662552
  7. Pfeifer, P. E. and Deutsch, S. J. (1980a). A three-stage iterative procedure for space-time modeling, Technometrics, 22, 35-47 https://doi.org/10.2307/1268381
  8. Pfeifer, P. E. and Deutsch, S. J. (1980b). Identification and Interpretation of first order space-time ARMA models, Technometrics, 22, 397-408 https://doi.org/10.2307/1268325

피인용 문헌

  1. Kalman-Filter Estimation and Prediction for a Spatial Time Series Model vol.18, pp.1, 2011, https://doi.org/10.5351/CKSS.2011.18.1.079
  2. The Development of the Short-Term Predict Model for Solar Power Generation vol.33, pp.6, 2013, https://doi.org/10.7836/kses.2013.33.6.062
  3. A new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model and spatiotemporal trend prediction analysis for Hemorrhagic Fever with Renal Syndrome (HFRS) vol.13, pp.11, 2018, https://doi.org/10.1371/journal.pone.0207518