時間序列資料分類近年來在機器學習和資料探勘上變成越來越重要的議題，而在時間序列資料分析時最重要的步驟是如何去提取特徵，並重新表示這種資料型態。一般來說，我們可以視每個时间戳記點為一個特徵，然而這樣的方法會遇到序列偏移、歪曲以及高維度問題。有鑑於這些問題對時間序列資料的影響，在這篇論文中我們提出一種通用的特徵提取架構，從 "文件探勘"中而來的 Bag-of-Words 概念在電腦視覺上也獲得蠻好的成果，因此我們將此應用於表示時間序列資料。從原始的時間序列中擷取出局部特徵來學習字典，並利用這些具有描述局部特徵能力的字典來重新表示時間序列資料。在我們的實驗中， 我們以 UCR time series collection datasets 所提供的資料來做評估，並與其他已知的方法做比較， 評估我們的時間序列特徵表示法。最後將此方法應用於開門實驗中，在門上裝置加速度儀測量每個人開門的行為，預期每個人開門會有不同的行為模式，最後用此論文的方法來做到很好的辨識開門的使用者。

Time series classification has attracted increasing attention in machine learning and data mining. In the analysis of time series data, how to represent data is a critical step for the performance. Generally, we can regard each value as a feature dimension for time series data instance. However, this naive representation might be not suitable because time series data usually be shifted, distorted and scaled in time. To address these problems, we proposed a general time series feature extraction framework. Since the concept of “Bag-of-Words” from text mining has shown promising performance in computer vision, we apply it to represent time series data. The subsequences from raw series were extracted as local patterns for learning codebook. Consequently, we encode a time series data instance by the codebook, which describes different local patterns of time series data. In our experiments, we demonstrate that our proposed method can achieve better results in UCR time series collection datasets by comparing with competitive methods. Finally, we apply this method to door opening experiment. The accelerometer is attached at the door to collect user door opening trajectory. We assume that each person has own pattern of opening trajectory. This method also makes promising performance for user identification.

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