CA-Smooth: Content Adaptive Smoothing of Time Series Leveraging Locally Salient Temporal Features
CA-Smooth: Content Adaptive Smoothing of Time Series Leveraging Locally Salient Temporal Features
复制标题
CA-Smooth：利用局部显着时间特征的时间序列的内容自适应平滑
DOI：
10.1145/3297662.3365830
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Sapino, Maria Luisa
中科院分区：
文献类型：
--
作者：
Rossini, Rosaria;Poccia, Silvestro;Candan, K. Selcuk;Sapino, Maria Luisa
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.
自动平滑可以显着改善基准时间序列分类算法吗？
DOI：
10.1007/978-3-030-29859-3_5
发表时间：
2018
期刊：
ArXiv
影响因子：
--
作者：
J. Large;Paul Southam;A. Bagnall
通讯作者：
A. Bagnall