@inproceedings{301d924e7b3d46079a47d6145b60b3f3,
title = "CA-smooth: Content adaptive smoothing of time series leveraging locally salient temporal features",
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.",
keywords = "Salient features, Smoothing, Time series",
author = "Rosaria Rossini and Silvestro Poccia and Candan, {K. Selcuk} and Sapino, {Maria Luisa}",
note = "Funding Information: *Research is supported by NSF1909555 “pCAR: Discovering and Leveraging Plausibly Causal (p-causal) Relationships to Understand Complex Dynamic Systems”, NSF1827757 “Data-Driven Services for High Performance and Sustainable Buildings”, NSF1610282 “DataStorm: A Data Enabled System for End-to-End Disaster Planning and Response”, NSF1633381 “BIGDATA: Discovering Context-Sensitive Impact in Complex Systems”, and “FourCmodeling”: EU-H2020 Marie Sklodowska-Curie grant agreement No 690817. Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 11th International Conference on Management of Digital EcoSystems, MEDES 2019 ; Conference date: 12-11-2019 Through 14-11-2019",
year = "2019",
month = nov,
day = "12",
doi = "10.1145/3297662.3365830",
language = "English (US)",
series = "11th International Conference on Management of Digital EcoSystems, MEDES 2019",
publisher = "Association for Computing Machinery, Inc",
pages = "36--43",
booktitle = "11th International Conference on Management of Digital EcoSystems, MEDES 2019",
}