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Imprecision and noise in the time series data may result in series with similar overall behaviors being recognized as being dissimilar because of the accumulation of many small local differences in noisy observations. While smoothing techniques can be used for eliminating such noise, the degree of smoothing that needs to be performed may vary significantly at different parts of the given time series. In this paper, we propose a content-adaptive smoothing, CA-Smooth, technique to reduce the
doi:10.1145/3297662.3365830
dblp:conf/medes/RossiniPCS19
fatcat:cowymktktjg3nk34zwijbtyzzi