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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/60094


    Title: 希爾柏特黃轉換於非穩定時間序列之分析:用電量與黃金價格
    Non-stationary time series analysis by using Hilbert-Huang transform: electricity consumption and gold price volatility
    Authors: 張雁茹
    Chang, Yen Rue
    Contributors: 蕭又新
    Shiau, Yuo Hsien
    張雁茹
    Chang, Yen Rue
    Keywords: 希爾柏特黃轉換
    經驗模態分解法
    用電量
    氣溫
    黃金價格
    Hilbert-Huang transform
    Empirical mode decomposition
    electricity consumption
    temperature
    gold price
    Date: 2011
    Issue Date: 2013-09-04 15:27:52 (UTC+8)
    Abstract:   本文有兩個研究目標,第一個是比較政大用電量與氣溫之間的相關性,第二則是分析影響黃金價格波動的因素。本文使用到的研究方法有希爾柏特黃轉換(HHT)與一些統計值。
      本研究使用的分析數據如下:政大逐時用電量、台北逐時氣溫以及倫敦金屬交易所(London Metal Exchange)的月平均黃金價格。透過經驗模態分解法(EMD),我們可以將分析數據拆解成數個互相獨立的分量,再藉由統計值選出較重要的分量並分析其意義。逐時用電量的重要分量為日分量、週分量與趨勢;逐時氣溫的重要分量為日分量與趨勢;月平均黃金價格的重要分量則是低頻分量與趨勢。
    藉由這些重要分量,我們可以更加了解原始數據震盪的特性,並且選出合理的平均週期將所有的分量分組,做更進一步的分析。逐時用電量與逐時氣溫分成高頻、中頻、低頻與趨勢四組,其中低頻與趨勢相加的組合具有最高的相關性。月平均黃金價格則是分為高頻、低頻與趨勢三組,其中高頻表現出供需以及突發事件等短週期因素,低頻與歷史上對經濟有重大影響的事件相對應,趨勢則是反應出通貨膨脹的現象。
      There are two main separated researched purposes in this thesis. First one is comparing the correlation between electricity consumption and temperature in NCCU. Another one is analyzing the properties of gold price volatility. The methods used in the study are Hilbert-Huang transform (HHT) and some statistical measures.
      The following original data: hourly electricity consumption in NCCU, hourly temperature in Taipei, and the LME monthly gold prices are decomposed into several components by empirical mode decomposition (EMD). We can ascertain the significant components and analyze their meanings or properties by statistical measures. The significant components of each data are shown as follows: daily component, weekly component and residue for hourly electricity consumption; daily component and residue for hourly temperature; low frequency components and residue for the LME monthly gold prices.
      We can understand more properties about these data according to the significant components, and dividing the components into several terms based on reasonable mean period. The components of hourly electricity consumption and hourly temperature are divided into high, mid, low frequency terms and trends, and the composition of low frequency terms and trends have the highest correlation between them. The components of LME monthly gold prices are divided into high, low frequency term and trend. High frequency term reveals the supply-demand and abrupt events. The low frequency term represents the significant events affecting economy seriously, and trend shows the inflation in the long run.
    Reference: Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., Liu, H.H., 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A 454 (1971), 903–995.

    Hwang, P.A., Huang, N.E., Wang, D.W., 2003. A note on analyzing nonlinear and non-stationary ocean wave data. Applied Ocean Research 25 (4), 187–193.

    Ray Ruichong Zhang, Shuo Ma, and Stephen Hartzell., 2003. Signatures of the Seismic Source in EMD-Based Characterization of the 1994 Northridge, California, Earthquake Recordings. Bulletin of the Seismological Society of America; February 2003; v. 93; no. 1; p. 501-518.

    Ray Ruichong Zhang, M.ASCE; Shuo Ma; Erdal Safak, M.ASCE; and Stephen Hartzell., 2003. Hilbert-Huang Transform Analysis of Dynamic and earthquake motion recordings. Journal of Engineering Mechanics, Vol. 129, No. 8, pp. 861-875.

    Li, Q.S., Wu, J.R., 2007. Time–frequency analysis of typhoon effects on a 79-storey
    tall building. Journal of Wind Engineering and Industrial Aerodynamics 95 (12), 1648–1666.

    Liang, H., Lin, Q.-H., Chen, J.D.Z., 2005. Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease. IEEE Transactions on Biomedical Engineering 52 (10), 1692–1701.

    Ruqiang Yan, Student Member, IEEE, and Robert X. Gao, Senior Member,IEEE., 2006. Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring. IEEE Transactions on instrumentation and measurement, Vol. 55, No. 6.

    Huang, N.E., Wu, M.L., Qu, W.D., Long, S.R., Shen, S.S.P., 2003b. Applications of Hilbert–Huang transform to nonstationary financial time series analysis. Applied Stochastic Models in Business and Industry 19, 245–268.

    Chen, M.-C., Wei, Y. Exploring time variants for short-term passenger flow. J. Transp. Geogr. (2010), doi:10.1016/j.jtrangeo.2010.04.003

    Cummings, D.A.T., Irizarry, R.A., Huang, N.E., Endy, T.P., Nisalak, A., Ungchusak, K., Burke, D.S., 2004. Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand. Nature 427 (6972), 344–347.

    Lean Yu, Shouyang Wang, Kin Keung Lai., 2008. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics 30 (2008) 2623–2635.


    Wu, Z., and N. E Huang (2004), Ensemble Empirical Mode Decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis. Vol.1, No.1. 1-41.

    Peel, M.C., Amirthanathan, G.E., Pegram, G.G.S., McMahon, T.A., Chiew, F.H.S., 2005. Issues with the application of empirical mode decomposition. In: Zerger, A., Argent, R.M. (Eds.), Modsim 2005 International Congress on Modelling and Simulation, pp. 1681–1687.
    Description: 碩士
    國立政治大學
    應用物理研究所
    98755005
    100
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0098755005
    Data Type: thesis
    Appears in Collections:[應用物理研究所 ] 學位論文

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