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題名 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作者 張雁茹
Chang, Yen Rue貢獻者 蕭又新
Shiau, Yuo Hsien
張雁茹
Chang, Yen Rue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
Hilbert-Huang transform
Empirical mode decomposition
electricity consumption
temperature
gold price日期 2011 上傳時間 4-Sep-2013 15:27:52 (UTC+8) 摘要 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.參考文獻 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-storeytall 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.2010.04.003Cummings, 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. 描述 碩士
國立政治大學
應用物理研究所
98755005
100資料來源 http://thesis.lib.nccu.edu.tw/record/#G0098755005 資料類型 thesis dc.contributor.advisor 蕭又新 zh_TW dc.contributor.advisor Shiau, Yuo Hsien en_US dc.contributor.author (Authors) 張雁茹 zh_TW dc.contributor.author (Authors) Chang, Yen Rue en_US dc.creator (作者) 張雁茹 zh_TW dc.creator (作者) Chang, Yen Rue en_US dc.date (日期) 2011 en_US dc.date.accessioned 4-Sep-2013 15:27:52 (UTC+8) - dc.date.available 4-Sep-2013 15:27:52 (UTC+8) - dc.date.issued (上傳時間) 4-Sep-2013 15:27:52 (UTC+8) - dc.identifier (Other Identifiers) G0098755005 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/60094 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 應用物理研究所 zh_TW dc.description (描述) 98755005 zh_TW dc.description (描述) 100 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 1. Introduction 11.1. Background 11.2. Purpose of research 31.3. Structure 42. Methodology 62.1. Empirical mode decomposition 62.1.1. Introduction to empirical mode decomposition 62.1.2. Intrinsic mode functions and sifting process 72.1.3. Ensemble Empirical Mode Decomposition 112.2. Statistical Measures 152.2.1. Mean period 162.2.2. Pearson product moment correlation coefficient 172.2.3. Kendall tau rank correlation coefficient 192.2.4. Variance 202.2.5. Power percentage and variance percentage 212.2.6 LRCV 223. Data and analysis 233.1. Data 243.1.1. Hourly electricity consumption in NCCU 243.1.2. Hourly temperature in Taipei 243.1.3. Monthly gold price 253.2. Hourly temperature in Taipei 263.3. Hourly electricity consumption in NCCU 303.3.1. Original data 363.3.2. Significant IMFs and statistics 383.3.3. Residues 413.4. Monthly gold price 423.5. Conclusion of analysis 444. Comparison between electricity consumption and temperature 464.1. Composition of low frequency terms and trends 514.1.1. Trends 514.1.2. Low frequency terms 534.1.3. The compositions 564.2. Mid frequency terms 594.3. High frequency term 615. Composition of monthly gold prices 675.1. Trend 695.2. Occurrence of significant events 715.3. Short-time factors and abrupt events 726. Conclusion and outlook 76Appendix 78Shorter-period returns for gold prices 78References 81 zh_TW dc.format.extent 2215634 bytes - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0098755005 en_US dc.subject (關鍵詞) 希爾柏特黃轉換 zh_TW dc.subject (關鍵詞) 經驗模態分解法 zh_TW dc.subject (關鍵詞) 用電量 zh_TW dc.subject (關鍵詞) 氣溫 zh_TW dc.subject (關鍵詞) 黃金價格 zh_TW dc.subject (關鍵詞) Hilbert-Huang transform en_US dc.subject (關鍵詞) Empirical mode decomposition en_US dc.subject (關鍵詞) electricity consumption en_US dc.subject (關鍵詞) temperature en_US dc.subject (關鍵詞) gold price en_US dc.title (題名) 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 zh_TW dc.title (題名) Non-stationary time series analysis by using Hilbert-Huang transform: electricity consumption and gold price volatility en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) 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-storeytall 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.2010.04.003Cummings, 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. zh_TW
