CA-Smooth

Rosaria Rossini, Silvestro Poccia, K. Selcuk Candan, Maria Luisa Sapino
2019 Proceedings of the 11th International Conference on Management of Digital EcoSystems  
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
more » ... t of non-informative details and noise in time series by means of a data-driven approach to smoothing. The proposed smoothing process treats different parts of the time series according to local information content. We show the impact of different adaptive smoothing criteria on a number of samples from different datasets, containing series with diverse characteristics.
doi:10.1145/3297662.3365830 dblp:conf/medes/RossiniPCS19 fatcat:cowymktktjg3nk34zwijbtyzzi