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Comparison Studies of Hybrid and Non-hybrid Forecasting Models for Seasonal and Trend Time Series Data
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Chulwoo Jeong (Korea Institute for Defense Analyses)
Myung Suk Kim (Global Service Management Department, Sogang Business School)
Vol. 19, No. 1, Page: 1 ~ 17
10.13088/jiis.2013.19.1.001
Keywords
Forecasting, Generalized Additive Models, Seasonal ARIMA, Hybrid Models, Hybrid Models
Abstract
In this article, several types of hybrid forecasting models are suggested. In particular, hybrid models using the generalized additive model (GAM) are newly suggested as an alternative to those using neural networks (NN). The prediction performances of various hybrid and non-hybrid models are evaluated using simulated time series data. Five different types of seasonal time series data related to an additive or multiplicative trend are generated over different levels of noise, and applied to the forecasting evaluation. For the simulated data with only seasonality, the autoregressive (AR) model and the hybrid AR-AR model performed equivalently very well. On the other hand, if the time series data employed a trend, the SARIMA model and some hybrid SARIMA models equivalently outperformed the others. In the comparison of GAMs and NNs, regarding the seasonal additive trend data, the SARIMA-GAM evenly performed well across the full range of noise variation, whereas the SARIMA-NN showed good performance only when the noise level was trivial.
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트렌드와 계절성을 가진 시계열에 대한 순수 모형과 하이브리드 모형의 비교 연구
정철우 (한국국방연구원 국방운영연구센터)
김명석 (서강대학교 경영전문대학원 글로벌 서비스 경영학과)
Abstract
본 연구에서는 시계열 예측을 위해 선형 모형과 비선형 모형의 하이브리드 모형 및 순수 모형의 성과를 비교?평가하였다. 이를 위해 5가지 서로 다른 패턴을 가지는 데이터를 생성하여 시뮬레이션을 진행하였다. 본 연구에서 고려한 선형 모형은 AR(autoregressive model)과 SARIMA(seasonal autoregressive integrated moving average model)이고, 비선형 모형은 인공신경망(artificial neural networks model)과 GAM(generalized additive model)이다. 특히, GAM은 여러 장점에도 불구하고 시계열 예측을 위한 비선형 모형으로 기존 연구들에서는 거의 쓰이지 않았던 모형이다. 시뮬레이션 결과, seasonality를 가지는 시계열에 대해서는 AR 및 AR-AR 모형이, trend를 가지는 시계열에 대해서는 SARIMA 및 SARIMA와 다른 모형의 하이브리드 모형이 다른 모형에 비해 높은 성과를 보였다. 한편, 인공신경망과 GAM을 비교하면, 트렌드와 계절성이 더해진 시계열에 대해 SARIMA와 GAM의 하이브리드 모형이 거의 모든 노이즈(noise) 수준에 대해 높은 성과를 보인 반면, 노이즈 수준이 미미한 경우에 한해 SARIMA와 인공신경망의 하이브리드 모형이 높은 성과를 보였다.
Cite this article
JIIS Style
Jeong, C., and M. S. Kim, "Comparison Studies of Hybrid and Non-hybrid Forecasting Models for Seasonal and Trend Time Series Data", Journal of Intelligence and Information Systems, Vol. 19, No. 1 (2013), 1~17.

IEEE Style
Chulwoo Jeong, and Myung Suk Kim, "Comparison Studies of Hybrid and Non-hybrid Forecasting Models for Seasonal and Trend Time Series Data", Journal of Intelligence and Information Systems, vol. 19, no. 1, pp. 1~17, 2013.

ACM Style
Jeong, C., and Kim, M. S., 2013. Comparison Studies of Hybrid and Non-hybrid Forecasting Models for Seasonal and Trend Time Series Data. Journal of Intelligence and Information Systems. 19, 1, 1--17.
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@article{Jeong:JIIS:2013:517,
author = {Jeong, Chulwoo and Kim, Myung Suk},
title = {Comparison Studies of Hybrid and Non-hybrid Forecasting Models for Seasonal and Trend Time Series Data},
journal = {Journal of Intelligence and Information Systems},
issue_date = {March 2013},
volume = {19},
number = {1},
month = Mar,
year = {2013},
issn = {2288-4866},
pages = {1--17},
url = {http://dx.doi.org/10.13088/jiis.2013.19.1.001 },
doi = {10.13088/jiis.2013.19.1.001},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Forecasting, Generalized Additive Models, Seasonal ARIMA, Hybrid Models and Hybrid Models },
}
%0 Journal Article
%1 517
%A Chulwoo Jeong
%A Myung Suk Kim
%T Comparison Studies of Hybrid and Non-hybrid Forecasting Models for Seasonal and Trend Time Series Data
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 19
%N 1
%P 1-17
%D 2013
%R 10.13088/jiis.2013.19.1.001
%I Korea Intelligent Information System Society