The diploma thesis is pursuing the analysis of principles on which the technical analysis is based, with focus on the relevancy. The attention will be paid to the fact whether technical analysis is able to generate successful trade signals and thus be recognized as scientific discipline. We will try to remove the subjectivity (for which is often criticized) by means of statistical methods. The diploma also includes basics of psychological analysis (especially new stances). In work, I evaluate whether technical analysis can be informative and add value to investment process. I also compare the returns based on technical analysis with Buy and Hold strategy.

Early identification and detection of abnormal time series patterns is vital for a number of manufacturing. Slide shifts and alterations of time series patterns might be indicative of some anomaly in the production process, such as machinery malfunction. Usually due to the continuous flow of data monitoring of manufacturing processes requires automated Control Chart Pattern Recognition(CCPR) algorithms. The majority of CCPR literature consists of supervised classification algorithms. Less studies consider unsupervised versions of the problem. Despite the profound advantage of unsupervised methodology for less manual data labeling their use is limited due to the fact that their performance is not robust enough for practical purposes. In this study we propose the use of a consensus clustering framework. Computational results show robust behavior compared to individual clustering algorithms.

越來越多帶有時間序列的資料普遍的存在醫學工程、商業統計、財務金融等各領域，例如：在財務金融分析領域中已知的形狀樣式用以預測未來價格趨勢做出買賣的決策。由於時序性資料通常非常的龐大，領域的專家看法也未必相同，所描述出新的形狀樣式剛開始也都是比較粗略的，必須透過不斷的修正才會得到比較精準的結果。有鑒於此，我們實做了一套時序性資料集的形狀查詢語言，透過簡單的語言描述，讓使用者簡便快速的定義出屬於自己的形狀樣式。此外我們也實作出互動式的環境並實際有效率應用於台灣證券交易市場。 / There are more and more time series data in the fields of medical engineering, commerce statistics, finance, etc. For example, in financial analysis, we can forecast the price trends by using some well known chart patterns. People want to find out some new patterns for making their purchase decisions fast and easily. However, it is technical challenging to implement a high-level pattern description language. This thesis implemented a shape query language for time-series datasets. Through the simple syntax, field users can find out there own shape patterns by using a more realistic, easily and fast way. We have also developed an interactive environment that users can apply our shape query language to the data of Taiwan Stock Market efficiently.

Analysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties create bias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive learning method (CSL) to deal with the classification of imperfect data. Typically, most traditional approaches for classification demonstrate poor performance in an environment with imperfect data. We propose the use of CSL with Support Vector Machine, which is a well-known data mining algorithm. The results reveal that the proposed algorithm produces more accurate classifiers and is more robust with respect to imperfect data. Furthermore, we explore the best performance measures to tackle imperfect data along with addressing real problems in quality control and business analytics.