Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
권호 표지
DOI QR코드

DOI QR Code

Determination of Pattern Models using a Convergence of Time-Series Data Conversion Technique for the Prediction of Financial Markets

금융시장 예측을 위한 시계열자료의 변환기법 융합을 이용한 패턴 모델 결정

  • Jeon, Jin-Ho (Dept. of Business Administration, Catholic Kwan-Dong University) ;
  • Kim, Min-Soo (Dept. of International Trade, Catholic Kwan-Dong University)
  • 전진호 (가톨릭관동대학교 경영학과) ;
  • 김민수 (가톨릭관동대학교 무역학과)
  • Received : 2015.03.16
  • Accepted : 2015.05.20
  • Published : 2015.05.28
Download PDF
⟨ Previous Next ⟩

Abstract

Export-led policies, FTA signed and economics of scale through a variety of market-oriented policies, such as regulations to improve market grew constantly. Accordingly, the correct decision making accurately analyze the economics market for decision, a problem has been an important issue in predicting. For accurate analysis and decision-making of the most common indicators of the stock market by proposing a number of indicators of economic transformation techniques were applied to the convergence model combining estimation and forecasts problem confirmed its effectiveness. Experimental result, gave the model estimation method to apply a transform to show the valid combinations proposed model state estimation result was confirmed in a very similar exercise aspect of the physical problem and the KOSPI index prediction.

수출주도정책, FTA 체결 및 규제개선 등과 같은 다양한 시장지향적인 정책을 통해 경제시장의 규모가 지속적으로 커졌다. 이에 따라 올바른 의사결정을 위하여 경제시장을 정확하게 분석, 예측하는 문제가 중요한 이슈가 되었다. 경제시장을 표현하는 여러 지표 중 가장 대표적인 주식지표의 정확한 분석 및 의사결정을 위하여 시계열자료의 모델링에 적합한 은닉마아코프모델을 토대로 자료 내에 내재된 예외적인 특징과 잡음을 제거하기 위한 변환기법의 융합모델을 제안하여 모델 추정과 예측 문제에 적용하였으며 그 유효성을 확인하였다. 실험 결과를 통해, 본 연구에서 제안하는 변환조합을 적용하는 모델추정 기법이 유효한 모델 상태 추정 결과를 보여주었으며 실제 코스피지수와 예측의 문제에서도 매우 유사한 운동양태를 확인할 수 있었다.

Keywords

References

  1. J. Jeon and m. Kim.,"A Study of Economic Indicator Prediction Model using Dimensions Decrease Techniques and HMM",The Journal of Digital Policy & Management, Vol. 11, no 10, pp305-311. 2013.
  2. S. Shin.,"Evaluation Exchange Rate of Artificial Neural Network and Moving Average Method", Finance Research, Vol. 9, no. 1, pp103-135, 1995.
  3. J. Jeon and m. Kim.,"A Study on Prediction the Movement Pattern of Time Series Data using Information Criterion and Effective Data Length", The Institute of Webcasting, Internet and Telecommunication, Vol. 13, no. 1, pp 101-107, 2013.
  4. J. Jeon., "A Study on Determining Prediction Models using Model-based Clustering of Time Series Data", Dankook Univ Ph. D, 2007.
  5. Y. Cho and G. Lee., "A Study on Improving Prediction Accuracy by Modeling Multiple Similar Time Series", The Institute of Webcasting, Internet and Telecommunication, Vol. 10, no. 6, pp 137-143, 2010.
  6. L. Rabiner., "A Tutorial on Hidden Markov Models and selected applications in speech recognition," Proc. of IEEE77, pp.257-286, 1989. https://doi.org/10.1109/5.18626
  7. A. Sorjamaa, et al., "Methodology for long-term prediction of time series," Neurocomputing, pp178-186. Elsevier, 2007.
  8. Y. Cho and G. Lee., "Prediction on Clusters by using Information Crtterion and Multiple Seeds", The Institute of Webcasting, Internet and Telecommunication, Vol. 10, no. 6, pp 145-152, 2010.
  9. M. Siddiqi, J. Gordon and W. Moore., "Fast State Discovery for HMM Moel Selection and Learning," In Proc. Int'l Conference on Artificial Intelligence and Statistics, 2007.
  10. J. Jeon and m. Kim.,"A study of criterion for efficient clustering estimation of temporal data", The Institute of Webcasting, Internet and Telecommunication, Vol. 11, no. 5, pp 139-144, 2011.
  11. A. Sorjamaa, et al., "Methodology for Long-Term Prediction of Time Series," Neurocomputing, pp178-186. Elsevier, 2007.
  12. L. Jessica, K. Eamonn, L. Stefano and C. Bill., "A Symbolic representation of time series, with impliction for streaming algorithms", 8th ACM SIGMOD Workshop on Research Issues in DMKD, 2003.
  13. Y. Byungki and F. Christos., "Fast Time Sequence Indexing for Arbitrary Lp Norms", In The VLDB Journal, pp 385-394, 2000.
  14. P. Cheeseman and J. Stutz,"Bayesian Classification"Kluwer Academic Publishers, Vol 70. pp117-126, 1996.
  15. Heckerman, D., Geiger, D., and Chekering, D. M. "A tutorial on learning with bayesian networks," machine Learning 20, pp.197-243, 1995.
  16. M. Kendall, Time Series, 2nd Edition, Charles Griffin and Company, 1979.
  17. C. Chatfield, The Analysis of Time-Series: An Introduction, 3rd Edition, Chapman and Hall, 1984.
  18. D. Rafiei and A. Mendelzon, "Similarity-Based Quries for Time Series Data," In Proc Int'l Conf. on Management of Data, ACM SIGMOD, pp13-24, 1997.
  19. R. Agrawal et al., "Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time Series Databases," In Proc. Int'l Conference on Very Large Databases, VLDB, pp, 490-501, 1995.
  20. W. K. Loh, S. W. Kim, and K. Y. Whang, "Index Interpolation: An Approach for Subsequence Matching Supporting Normalization Transform in Time-Series Databases, 2000.