A New Time Series Representation Model and Corresponding Similarity Measure for Fast and Accurate Similarity Detection

Data representation and similarity measurement are two basic aspects of similarity detection in time series data mining. In this paper, we present two novel approaches to perform similarity detection efficiently and effectively. One is composed of a new time series representation model and a corresponding similarity measure, which is called fragment alignment distance (FAD); the other applies dynamic time warping to the representation model of FAD and is called FAD_DTW. The new data representation model is based on the trend information of time series, which can provide a concise yet feature-rich representation of time series. FAD is able to align the segments of time series in linear time, which greatly accelerates the similarity detection process. We extensively compare FAD and FAD_DTW with state-of-the-art time series representation models and similarity measures in classification and clustering frameworks. Experimental results from efficiency and effectiveness validations on various data sets demonstrate that FAD and FAD_DTW can achieve fast and accurate similarity detection. In particular, FAD is much faster than the other methods.