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

簡易檢索 / 詳目顯示

回結果列表

研究生：	張雁茹 Chang, Yen Rue
論文名稱：	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
指導教授：	蕭又新 Shiau, Yuo Hsien
學位類別：	碩士 Master
系所名稱：	理學院 - 應用物理研究所 Graduate Institute of Applied Physics
論文出版年：	2011
畢業學年度：	99
語文別：	英文
論文頁數：	91
中文關鍵詞：	希爾柏特黃轉換、經驗模態分解法、用電量、氣溫、黃金價格
外文關鍵詞：	Hilbert-Huang transform, Empirical mode decomposition, electricity consumption, temperature, gold price
相關次數：	點閱：318 下載：30
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

　　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.

1. Introduction 1
1.1. Background 1
1.2. Purpose of research 3
1.3. Structure 4
2. Methodology 6
2.1. Empirical mode decomposition 6
2.1.1. Introduction to empirical mode decomposition 6
2.1.2. Intrinsic mode functions and sifting process 7
2.1.3. Ensemble Empirical Mode Decomposition 11
2.2. Statistical Measures 15
2.2.1. Mean period 16
2.2.2. Pearson product moment correlation coefficient 17
2.2.3. Kendall tau rank correlation coefficient 19
2.2.4. Variance 20
2.2.5. Power percentage and variance percentage 21
2.2.6 LRCV 22
3. Data and analysis 23
3.1. Data 24
3.1.1. Hourly electricity consumption in NCCU 24
3.1.2. Hourly temperature in Taipei 24
3.1.3. Monthly gold price 25
3.2. Hourly temperature in Taipei 26
3.3. Hourly electricity consumption in NCCU 30
3.3.1. Original data 36
3.3.2. Significant IMFs and statistics 38
3.3.3. Residues 41
3.4. Monthly gold price 42
3.5. Conclusion of analysis 44
4. Comparison between electricity consumption and temperature 46
4.1. Composition of low frequency terms and trends 51
4.1.1. Trends 51
4.1.2. Low frequency terms 53
4.1.3. The compositions 56
4.2. Mid frequency terms 59
4.3. High frequency term 61
5. Composition of monthly gold prices 67
5.1. Trend 69
5.2. Occurrence of significant events 71
5.3. Short-time factors and abrupt events 72
6. Conclusion and outlook 76
Appendix 78
Shorter-period returns for gold prices 78
References 81

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.

簡易檢索 / 詳目顯示

相關論文