【摘要】：時間序列分析廣泛應(yīng)用于各個領(lǐng)域,相似性研究是時間序列分析的基礎(chǔ),預(yù)測是時間序列分析中的重要問題。論文針對醫(yī)學(xué)中的時間序列數(shù)據(jù)和軌道交通客流時間序列數(shù)據(jù)的相似性與預(yù)測問題,對四個問題進行了研究和討論：腦卒中(腦中風(fēng))癥狀證候布爾時間序列的動態(tài)分析及預(yù)后判斷；腦卒中腦電數(shù)據(jù)的特征分析及預(yù)后判斷；腦卒中病灶位置及其腦電信號的特征分析；軌道交通客流數(shù)據(jù)的相似性及預(yù)測分析。論文的主要工作及創(chuàng)新點總結(jié)如下： 一、針對腦卒中癥狀及證候布爾時間序列數(shù)據(jù)進行了分析,從中醫(yī)的角度討論了腦中風(fēng)病癥狀及證候的布爾時間序列監(jiān)測數(shù)據(jù)的特點,提出了面向布爾時間序列數(shù)據(jù)的關(guān)聯(lián)規(guī)則挖掘算法,利用癥狀及證候數(shù)據(jù)的動態(tài)變化信息對患者的預(yù)后作出判斷。 二、針對腦卒中腦電時間序列數(shù)據(jù)進行了分析,從西方醫(yī)學(xué)的角度討論了正常腦電數(shù)據(jù)與異常腦中風(fēng)病腦電數(shù)據(jù)的特點,提出了腦電時間序列信息的雙側(cè)對稱度量指標(biāo),并以此提出了正常腦電與異常中風(fēng)腦電的判別方法和腦中風(fēng)病預(yù)后判別算法。 三、針對量化的腦卒中腦電時間序列的病灶位置進行了進一步分析,討論并對比了腦中風(fēng)病腦電序列的多種特征,提出了腦中風(fēng)病腦電時間序列病灶位置分區(qū)分析方法。 四、針對軌道交通客流時間序列數(shù)據(jù)進行了分析,主要討論了軌道交通客流數(shù)據(jù)的類周期性特點,對已有的城市道路交通流時間序列相似性與預(yù)測方法進行改進,并應(yīng)用于軌道交通客流數(shù)據(jù)分析,提出了基于相似模式的軌道交通客流長期預(yù)測算法。
[Abstract]:Time series analysis is widely used in various fields. Similarity analysis is the basis of time series analysis and prediction is an important problem in time series analysis. This paper aims at the similarity and prediction of time series data and passenger flow time series data in medical science. Four problems were studied and discussed in this paper: dynamic analysis and prognostic judgement of Boolean time series of stroke symptoms and syndromes, characteristic analysis and prognosis judgement of EEG data of stroke; The characteristic analysis of focal location and EEG signal of stroke, the similarity and prediction analysis of passenger flow data of rail transit. The main work and innovations of this paper are summarized as follows: firstly, the Boolean time series data of stroke symptoms and syndromes are analyzed. This paper discusses the characteristics of Boolean time series monitoring data of cerebral apoplexy symptoms and syndromes from the perspective of traditional Chinese medicine, and proposes an association rule mining algorithm for Boolean time series data. The prognosis of patients was judged by dynamic change information of symptoms and syndromes. Secondly, this paper analyzes the EEG time series data of stroke, discusses the characteristics of normal EEG data and abnormal cerebral apoplexy EEG data from the point of view of western medicine, and puts forward the bilateral symmetry metric index of EEG time series information. The method of distinguishing normal EEG from abnormal apoplexy and the algorithm of predicting the prognosis of stroke were put forward. Thirdly, the location of the focus of the time series of stroke is further analyzed, and various features of the EEG sequence of stroke are discussed and compared, and the method of location analysis of the lesion in the time series of stroke is put forward. Fourthly, the paper analyzes the time series data of rail transit passenger flow, mainly discusses the quasi-periodicity of the passenger flow data, and improves the similarity and prediction method of the existing urban road traffic flow time series. Based on the analysis of passenger flow data of rail transit, an algorithm of long-term passenger flow prediction based on similarity model is proposed.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：R743.3;O211.61

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