References
- Beran, J., Statistics for Long-Memory Processes, London, England : Chapman and Hall/CRC, 1994.
- Black, F. and Scholes, M., The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 1973, Vol. 81, p 637-654. https://doi.org/10.1086/260062
- Bollerslev, T., Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 1986, Vol. 31, p 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
- Choi, J.R., Hur, N.K., Kim, D.C., and Kim, H.S., A Numerical Study on the Improvement of the Performance of a Vehicle Paint Drying Process. Korean J Air-conditioning and Rdf Eng, 2012, Vol. 24, p 868-875.
- Cox, J., Ross, S., and Rubinstein, M., Option pricing : A simplified approach. Journal of Financial Economy, 1979, Vol. 7, p 229-263. https://doi.org/10.1016/0304-405X(79)90015-1
- Engle, R., Autoregressive Conditional Hetero scedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 1982, Vol. 50, p 987-1007. https://doi.org/10.2307/1912773
- Faranda, D. and Dubrull, B., Statistical early-warning indicators based on Auto-Regressive Moving-Average Processes, 2014, arXiv : 1402.2885v1.
- Kim, I.M., Kim, C.S., and Park, S.K., Forecasting the Energy Demand Responses to Relative Price Changes. Economic Studies, 2012, Vol. 59, p 199-228.
- Kong, D., Kwak, Y., and Lee, B., Huh, J.,. A Methodology of Databased Energy Demand Prediction Using Artificial Neural Networks for a Urban Community. Society of Korean Solar Energy, 2009, Vol. 29, p 184- 189.
- Lee, C., Detection of a long-range correlation with an adaptive detrending method. Phys Rev E, 2012, Vol. 86, 011135-5. https://doi.org/10.1103/PhysRevE.86.011135
- Lee, H., Park, K., and Shin, H., Electricity demand forecasting based on machine learning algorithms. Society of Korean management Science/Society of Korean Industrial Engineering Spring Joint Conference Proceedings, 2011, p 521-546.
- Liu, Y., Gopikrishnan, P., Cizeau, P., Meyer, M., Peng, C.K., and Stanley, H.E., Statistical properties of the volatility of price fluctuations. Physical Review E, 1999, Vol. 60, p 1390-1400. https://doi.org/10.1103/PhysRevE.60.1390
- Mills, T.C., Time Series Techniques for Economists. New York : Cambridge University Press, 1990.
- Pagan, A., The econometrics of financial markets. J Empirical Finance, 1996, Vol. 3, p 15-102. https://doi.org/10.1016/0927-5398(95)00020-8
- Peng, C.K., Buldyrev, S.V., Havlin, S., Simons, M., Stanley, H.E., and Goldberger, A.L., Mosaic organization of DNA nucleotides. Physical Review E, 1994, Vol. 49, p 1685-1689.
- Peng, C.K., Havlin, S., Stanley, H.E., Goldberger, A., Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos, 1995, Vol. 5, p 82-87. https://doi.org/10.1063/1.166141
- Song, G.S., Development of Air-Conditioning System for Energy Saving type vehicle drying. Report of DUKSAN Co. Ltd., 2013.
- Suganthi, L. and Samuel, A.A., Energy models for demand forecasting-A review. Renewable and Sustainable Energy Reviews, 2012, Vol. 16, p 1223-1240. https://doi.org/10.1016/j.rser.2011.08.014
- Suh, J., Lee, S., Oh. H., Koo, J., Lim, T., and Cho, J., ARMA-PL : Tackling Nested Periods and Linear Trend in Time Series Data. Journal of the Society of Korea Industrial and Systems Engineering, 2010, Vol. 33, p 112-126.
Cited by
- 엘만 순환 신경망을 사용한 전력 에너지 시계열의 예측 및 분석 vol.41, pp.1, 2014, https://doi.org/10.11627/jkise.2018.41.1.084
- 시간대별 기온과 전력 사용량의 민감도를 적용한 전력 에너지 수요 예측 vol.42, pp.1, 2019, https://doi.org/10.11627/jkise.2019.42.1.129