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.

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