Please use this identifier to cite or link to this item:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71311Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 任立中 | |
| dc.contributor.author | Hao Chang | en |
| dc.contributor.author | 張皓 | zh_TW |
| dc.date.accessioned | 2021-06-17T05:04:18Z | - |
| dc.date.available | 2018-07-26 | |
| dc.date.copyright | 2018-07-26 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-07-23 | |
| dc.identifier.citation | 一、 中文文獻
葉小蓁 1998 時間序列分析與應用: 葉小蓁發行. 二、 英文文獻 Agarwal, Rakesh, and Ramakrishnan Srikant 1994 Fast algorithms for mining association rules. Proc. of the 20th VLDB Conference, 1994, pp. 487-499. Borgelt, Christian 2005 An Implementation of the FP-growth Algorithm. Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations, 2005, pp. 1-5. ACM. Box, George EP, et al. 2015 Time series analysis: forecasting and control: John Wiley & Sons. Brin, Sergey, et al. 1997 Dynamic itemset counting and implication rules for market basket data. Acm Sigmod Record 26(2):255-264. Chen, Jason R 2007 Useful clustering outcomes from meaningful time series clustering. Proceedings of the sixth Australasian conference on Data mining and analytics-Volume 70, 2007, pp. 101-109. Australian Computer Society, Inc. Esling, Philippe, and Carlos Agon 2012 Time-series data mining. ACM Computing Surveys (CSUR) 45(1):12. Gavrilov, Martin, et al. 2000 Mining the stock market (extended abstract): which measure is best? Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 2000, pp. 487-496. ACM. Hyndman, Rob J, et al. 2018 Package ‘forecast’. Online] https://cran. r-project. org/web/packages/forecast/forecast. pdf. Hyndman, Rob J, and Yeasmin Khandakar 2007 Automatic time series for forecasting: the forecast package for R: Monash University, Department of Econometrics and Business Statistics. Imhoff, Michael, et al. 1998 Time series analysis in intensive care medicine. Technical Report, SFB 475: Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund. Keogh, Eamonn J, and Michael J Pazzani 2001 Derivative dynamic time warping. Proceedings of the 2001 SIAM International Conference on Data Mining, 2001, pp. 1-11. SIAM. Sun, Leilei, and Chonghui Guo 2014 Incremental affinity propagation clustering based on message passing. IEEE Transactions on Knowledge and Data Engineering 26(11):2731-2744. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71311 | - |
| dc.description.abstract | 在傳統的時間序列分析中，多是針對單一的時間序列進行自我相關迴歸分析，若要進行高維度的分析則可能遇上非定態(Non-Stationary)的情況，無法直接建立模型分析。因此發展出如 PCA 降維(Gavrilov, et al. 2000)、動態時間校正(Keogh and Pazzani 2001)等方法將高維時間序列進行分群（Clustering）。
本篇研究使用聯合醫院某院區為期兩年半之用藥資料進行分析，其中包含1038 種藥品在 30 個月中的藥品支出量，在對藥物不具有領域知識(Domain knowledge)的情況下，欲分別使用動態時間校正以及 ARIMA 模型建立各藥品的殘差矩陣兩方法，探詢各藥品之相關性，再從有相關性的藥品當中，實際查看時間序列上的趨勢（Trend）、季節性（Seasonal）的異同，或是序列相似的藥品間是否具有實際關係做後續分析。 | zh_TW |
| dc.description.abstract | In traditional time series methods, studies usually used ARIMA model to analysis and predict. However, it may become non-stationary model in high dimension situation
so that the study can’t use multivariate ARIMA model directly. Reducing dimensions by PCA (principal component analysis) and DTW (dynamic time warping) are another way to cluster high dimensions time series data. Data of the theme is the medicine expenditure from TAIPEI CITY HOSPITAL. It is from January, 2015 to June, 2017, includes 1038 drugs. Without domain knowledge of drugs, the study uses DTW and residual matrix of ARIMA model individually to find correlated drugs. After finds out correlated drugs, the author checks if these drugs have similarity of trend or seasonal. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T05:04:18Z (GMT). No. of bitstreams: 1 ntu-107-R05h41010-1.pdf: 1149559 bytes, checksum: 14173d433b1b9fbcce0e5d0afcfd0ae2 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 口試委員會審定書...II
誌 謝...III 摘 要...IV Abstract...V 目 錄...VI 表目錄...VII 圖目錄...VIII 第壹章 緒論...9 第一節 研究背景與動機...9 第二節 研究目的...9 第三節 研究流程...10 第貳章 文獻回顧...11 第一節 醫院用藥分析之文獻回顧...11 第二節 動態時間校正分析之文獻回顧...12 第三節 模型基底分析之文獻回顧...13 第參章 研究方法...16 第一節 資料來源...16 第二節 研究架構...16 第三節 分析方法...18 第肆章 實證分析...19 第一節 探索性分析...19 第二節 動態時間校正分析...20 第三節 模型基底方法分析...23 第四節 結果解釋...25 第伍章 結論與建議...26 第一節 結論...26 第二節 建議...26 參考文獻...28 | |
| dc.language.iso | zh-TW | |
| dc.subject | 時間序列相關性 | zh_TW |
| dc.subject | 高維度時間序列 | zh_TW |
| dc.subject | 動態時間校正 | zh_TW |
| dc.subject | ARIMA 模型 | zh_TW |
| dc.subject | 殘差分析 | zh_TW |
| dc.subject | ARIMA model | en |
| dc.subject | time series correlation | en |
| dc.subject | residual analysis | en |
| dc.subject | high dimension time series | en |
| dc.subject | dynamic time warping | en |
| dc.title | 動態時間校正與模型基底分析高維度時間序列資料 | zh_TW |
| dc.title | Large Scale Time Series Data Analysis by Using Dynamic Time
Warping and Model-Based Method | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 王鴻龍,陳靜怡 | |
| dc.subject.keyword | 時間序列相關性,高維度時間序列,動態時間校正,ARIMA 模型,殘差分析, | zh_TW |
| dc.subject.keyword | time series correlation,high dimension time series,dynamic time warping,ARIMA model,residual analysis, | en |
| dc.relation.page | 29 | |
| dc.identifier.doi | 10.6342/NTU201801753 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-07-23 | |
| dc.contributor.author-college | 共同教育中心 | zh_TW |
| dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
| Appears in Collections: | 統計碩士學位學程 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-107-1.pdf Restricted Access | 1.12 MB | Adobe PDF |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.