پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 610 |
| تعداد مقالات | 9,028 |
| تعداد مشاهده مقاله | 67,082,871 |
| تعداد دریافت فایل اصل مقاله | 7,656,352 |
Forecasting enhancement using a Hodrick-Prescott filter | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، شماره 2، بهمن 2021، صفحه 2247-2262 اصل مقاله (884.27 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5372 | ||
| نویسندگان | ||
| Aseel Sameer Mohamed* 1؛ Nooriya A. Mohammed2 | ||
| 1Family and Community Medicine Department, Al Kindy Medical College, University of Baghdad, Iraq | ||
| 2Planning and Studies Office, Ministry of Electricity, Baghdad, Iraq | ||
| تاریخ دریافت: 29 اسفند 1399، تاریخ بازنگری: 27 خرداد 1400، تاریخ پذیرش: 07 مرداد 1400 | ||
| چکیده | ||
| Sound forecasts are essential elements of planning, especially for dealing with seasonality, sudden changes in demand levels, strikes, large fluctuations in the economy, and price-cutting manoeuvres for competition. Forecasting can help decision-makers to manage these problems by identifying which technologies are appropriate for their needs. The proposal forecasting model is utilized to extract the trend and cyclical component individually through developing the Hodrick--Prescott filter technique. Then, the fit models of these two real components are estimated to predict the future behaviour of electricity peak load. Accordingly, the optimal model obtained to fit the periodic component is estimated using spectrum analysis and the Fourier model, and the expected trend is obtained using simple linear regression models. Actual and generation data were used for the performance evaluation of the proposed model. The results of the current model, with improvement, showed higher accuracy as compared to ARIMA model performance. | ||
| کلیدواژه‌ها | ||
| forecasting؛ Spectrum Analysis؛ Hodrick-Prescott Filter | ||
| مراجع | ||
|
[1] S. Beveridge and C.R. Nelson, A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the ‘business cycle, J. Monetary Econ. 7(2) (1981) 151–174. [2] B. Buttkus, Spectral Analysis and Filter Theory in Applied Geophysics, Springer, 2000. [3] M. Ehrendorfer, Spectral Numerical Weather Prediction Models, Soc. Indust. Appl. Math. Univ. City Science Center Philadelphia, PAUnited States, 2011. [4] M. Fritz, T. Gries and Y. Feng, On enhanced alternatives for the Hodrick-Prescott filter, 2007 [5] A. Fumi, A. Pepe, L. Scarabotti and M.M. Schiraldi, Fourier analysis for demand forecasting in a fashion company, Int. J. Engin. Busin. Manag. 5 (2013) 5–30. [6] D. Grzesica and P. Wiecek, Advanced forecasting methods based on spectral analysis, Procedia Engin. 161 (2016) 253–258. [7] H. Hahn, S. Meyer-Nieberg and S. Pickl, Electric load forecasting methods: Tools for decision making, Euro. J. Oper. Res. 199(3) (2009) 902–907. [8] J.D. Hamilton, Why you should never use the Hodrick-Prescott filter, Rev. Econ. Stat. 100(5) (2018) 831–843. [9] R.J. Hodrick and E.C. Prescott, Postwar US business cycles: An empirical investigation, J. Money Credit Bank. (1997) 1–16.[10] A. Jmp and M. Proust, Specialized Models, Cary, NC: SAS, 2013. [11] R.G. King and S. Rebelo, Appendix to low frequency filtering and real business cycles, J. Econ. Dyn. Cont. 17 (1993) 207–231. [12] C.K. Kovach and P.E. Gander, The demodulated band transform, J. Neurosci. Meth. 261 (2016) 135–154. [13] A. Maravall and A. Del Rio, Temporal aggregation, systematic sampling, and the Hodrick–Prescott filter, Comput. Stat. Data Anal. 52 (2007) 975–998. [14] M.O. Ravn and H. Uhlig, H. On adjusting the Hodrick-Prescott filter for the frequency of observations, Rev. Econ. Stat. 84(2) (2002) 371–376. [15] A.K. Singh, S.K. Ibraheem, M. Muazzam and D. Chaturvedi, An overview of electricity demand forecasting techniques, Network Complex Syst. 3(3) (2013) 38–48. [16] T. T. Tchrakian, B. Basu and M. O’Mahony, Real-time traffic flow forecasting using spectral analysis, IEEE Trans. Intel. Transport. Syst. 13(2) (2011) 519–526. [17] Y. Zhang, Y. Zhang and A. Haghani, A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model, Transport. Res. Part C: Emerging Tech. 43 (2014) 65–78. | ||
|
آمار تعداد مشاهده مقاله: 15,590 تعداد دریافت فایل اصل مقاله: 687 |
||