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研究生： 王儀茹
論文名稱： 追蹤季節性時間數列模型之流程資料
Monitoring process data with seasonal time series model
指導教授： 楊素芬
鄭宗記
學位類別： 碩士
Master
系所名稱： 商學院 - 統計學系
Department of Statistics
論文出版年： 2013
畢業學年度： 101
語文別： 英文
論文頁數： 64
中文關鍵詞： 季節性時間數列 、信賴帶 、自我相關製程
外文關鍵詞： Seasonal time series model, Confidence band, Autocorrelated process
相關次數： 點閱：330  下載：41 
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    • 追蹤季節性時間數列模型之流程資料

    Control charts are designed and evaluated under the assumption that the observations from the process are independent and identically distributed. However, the independence assumption is often violated in practice. Autocorrelation may be represented in many processes. To solve this problem, it is becoming more common to obtain profiles at each time period. Profile monitoring is the use of control charts for cases in which the quality of a process or product can be characterized by a functional relationship between a response variable and one or more explanatory variables. For the data with seasonal time series model, we propose several monitoring approaches to detect the out-of-control profiles. After considering both Phase I and Phase II schemes, a real example is given to illustrate the results.

    Contents
    Chapter 1 Introduction 1
    Chapter 2 Research Methods 4
    2.1 Control Bands under each time unit 5
    2.1.1 Introduction 5
    2.1.2 Model assumptions and notations 5
    2.2 Confidence bands based on Kalman Filter approach 7
    2.2.1 Introduction 7
    2.2.2 Model assumption and notation 8
    2.2.3 State Space Model 10
    2.2.4 Kalman Filter 12
    2.3 Confidence Bands based on Bootstrap approach 15
    2.3.1 Introduction 15
    2.3.2 Bootstrap Procedure 16
    2.3.3 Control chart for monitoring variance of the residuals 16
    2.4 Hotelling T2 control charts 17
    2.4.1 Introduction 17
    2.4.2 Hotelling’s T2 Control Chart 17
    2.4.3 T2 chart for monitoring coefficients of a profile 18
    2.4.4 T2 chart for monitoring variance of the residuals 19
    Chapter 3 Real data analysis: Power Consumption in National Chengchi University 21
    3.1 Introduction 21
    3.2 Data classification 22
    3.3 Phase I and Phase II control scheme 30
    3.3.1 Model assumption and identification 30
    3.3.2 Phase I and Phase II control charts 35
    3.3.3 Example of Semester without AC data (WOAC data) 36
    3.3.4 Example of Semester with AC data (WAC data) 44
    3.4 Performance Comparison 52
    Chapter 4 Conclusions 61
    References 62

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