Abstract
We consider the similarity problem in time series databases. Given a set of sequences, we are interested in finding those sequences whose behaviors are similar. Several methods have been proposed so far to solve this problem. Among them, [AFS93] mapped a time sequence to the frequency domain using the Discrete Fourier Transformation (DFT), and kept only first few coefficients. The similarity of two sequences is determined by the Euclidean distance between their coefficients. [ALS+95] (hereafter referred to as ALS approach) introduced a new model. Two time-series are said to be similar if they have enough non-overlapping time-ordered pairs of subsequences that are similar. Different from the ALS approach, [DGM97] used another method to find the longest common subsequence. The idea is, given a set of transformation functions, to try to find a linear function y=ax+b such that two subsequences X, Y are e-similar. However no paper up to now, as to our knowledge, had compared these algorithms on the same benchmark. This paper discusses and compares these three popular methods. By overcoming the drawbacks of these algorithms, a new algorithm is proposed. Experiments are conducted on the Nasdaq-100 market.
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Wu, F., Plihon, V., Gardarin, G. (2002). Similarity-Based Queries for Time Series Databases. In: Jin, Q., Li, J., Zhang, N., Cheng, J., Yu, C., Noguchi, S. (eds) Enabling Society with Information Technology. Springer, Tokyo. https://doi.org/10.1007/978-4-431-66979-1_2
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DOI: https://doi.org/10.1007/978-4-431-66979-1_2
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