- Yiming Yang, Rong Pan, Sinno Jialin Pan, Qiang Yang, Lei Li. 2007. A Comparative Study on Time Series Classification. Abstract: Time series classification or categorization is an important task in time-series analysis. Unlike traditional methods and problem formulations in time-series analysis, time series classification aims to take whole time sequences as input, and produce discrete labels that are assigned to each sequence. Compared to traditional classification problems, time series classification poses additional difficulties. A major difficulty is due to the fact that the time sequences are variable in length, making many traditional classification methods unable to apply directly. Even for sequences of uniform lengths, many methods can still not be applied directly because often the data located at different parts of the sequences are incomparable. Two methods have been tried separately in the past, including distance based methods such as DTW, and model based methods such as Markov models. Using either of these methods as preprocessing steps, a uniform length vector space can be built to enable the classification methods to be applied. In the past, there has been a lack of comparison between these two methods. This paper compares distance and model based methods on several data sets including synthetic and real data sets, to explicate the relative advantages and disadvantages of these methods. This paper presents several key observations on the relative merits of these two methods, and paves the way for further research in developing new methods for time series classification.
