Dynamic time series smoothing for symbolic interval data applied to neuroscience

被引：7

作者：

Nascimento, Diego C. ^{[1
]}

Pimentel, Bruno ^{[1
]}

Souza, Renata ^{[2
]}

Leite, Joao P. ^{[3
]}

Edwards, Dylan J. ^{[4
,5
]}

Santos, Taiza E. G. ^{[3
]}

Louzada, Francisco ^{[1
]}

机构：

[1] Univ Sao Paulo, Inst Math Sci & Comp, Sao Carlos, Brazil

[2] Univ Fed Pernambuco, Ctr Informat, Recife, PE, Brazil

[3] Univ Sao Paulo, Ribeirao Preto Med Sch, Ribeirao Preto, Brazil

[4] Moss Rehabil Res Inst, Elkins Pk, PA USA

[5] Edith Cowan Univ, Sch Med & Hlth Sci, Joondalup, WA, Australia

来源：

INFORMATION SCIENCES | 2020年 / 517卷

基金：

巴西圣保罗研究基金会;

关键词：

State space model; Symbolic data analysis; Verticality perception; LINEAR-REGRESSION; ROBUST REGRESSION; MODELS;

D O I：

10.1016/j.ins.2019.12.026

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This work aimed to appraise a multivariate time series, high-dimensionality data-set, presented as intervals using a Symbolic Data Analysis (SDA) approach. SDA reduces data dimensionality, considering the complexity of the model information through a set-valued (interval or multi-valued). Additionally, Dynamic Linear Models (DLM) are distinguished by modeling univariate or multivariate time series in the presence of non-stationarity, structural changes and irregular patterns. We considered neurophysiological (EEG) data associated with experimental manipulation of verticality perception in humans, using transcranial electrical stimulation. The innovation of the present work is centered on use of a dynamic linear model with SDA methodology, and SDA applications for analyzing EEG data. (C) 2019 Elsevier Inc. All rights reserved.

引用

页码：415 / 426

页数：12

共 50 条

[21] The dynamic distribution smoothing technique based on time series analysis
College of Computer Science, Chongqing University, Chongqing 400044, China
不详
Tien Tzu Hsueh Pao, 2008, SUPPL. (147-151):
[22] Symbolic time-series analysis of neural data
Lesher, S
Guan, L
Cohen, AH
NEUROCOMPUTING, 2000, 32 (32-33) : 1073 - 1081
[23] Towards symbolic data mining in numerical time series
Santamaría, A
López-Illescas, A
Perez-Perez, A
Caraça-Valente, JP
RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEMS XXI, 2005, : 231 - 243
[24] Symbolic Time Series Representation for Stream Data Processing
Sevcech, Jakub
Bielikova, Maria
2015 IEEE TRUSTCOM/BIGDATASE/ISPA, VOL 2, 2015, : 217 - 222
[25] The Smoothing of Time Series
Crathorne, A. R.
JOURNAL OF POLITICAL ECONOMY, 1932, 40 (03) : 427 - 428
[26] A New Representation of Interval Symbolic Data and Its Application in Dynamic Clustering
Li, Wenhua
Guo, Junpeng
Chen, Ying
Wang, Minglu
JOURNAL OF CLASSIFICATION, 2016, 33 (01) : 149 - 165
[27] A New Representation of Interval Symbolic Data and Its Application in Dynamic Clustering
Wenhua Li
Junpeng Guo
Ying Chen
Minglu Wang
Journal of Classification, 2016, 33 : 149 - 165
[28] Normalization of Interval Symbolic Data
Guo, Junpeng
Li, Wenhua
Cheng, Sue
2009 IEEE 16TH INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1 AND 2, PROCEEDINGS, 2009, : 1769 - +
[29] Conformal Prediction Interval for Dynamic Time-Series
Xu, Chen
Xie, Yao
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139, 2021, 139
[30] Complexity analysis of stride interval time series by threshold dependent symbolic entropy
Wajid Aziz
Muhammad Arif
European Journal of Applied Physiology, 2006, 98 : 30 - 40

← 1 2 3 4 5 →