CA-smooth: Content adaptive smoothing of time series leveraging locally salient temporal features

Rosaria Rossini, Silvestro Poccia, K. Selcuk Candan, Maria Luisa Sapino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 impact 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.

Original languageEnglish (US)
Title of host publication11th International Conference on Management of Digital EcoSystems, MEDES 2019
PublisherAssociation for Computing Machinery, Inc
Pages36-43
Number of pages8
ISBN (Electronic)9781450362382
DOIs
StatePublished - Nov 12 2019
Event11th International Conference on Management of Digital EcoSystems, MEDES 2019 - Limassol, Cyprus
Duration: Nov 12 2019Nov 14 2019

Publication series

Name11th International Conference on Management of Digital EcoSystems, MEDES 2019

Conference

Conference11th International Conference on Management of Digital EcoSystems, MEDES 2019
CountryCyprus
CityLimassol
Period11/12/1911/14/19

Keywords

  • Salient features
  • Smoothing
  • Time series

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Networks and Communications
  • Environmental Engineering

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