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
收藏
页码：415 / 426
页数：12
相关论文
共 50 条
  • [1] Regression applied to symbolic interval-spatial data
    Freitas, Wanessa W. L.
    de Souza, Renata M. C. R.
    Amaral, Getulio J. A.
    de Moraes, Ronei M.
    APPLIED INTELLIGENCE, 2024, 54 (02) : 1545 - 1565
  • [2] Regression applied to symbolic interval-spatial data
    Wanessa W. L. Freitas
    Renata M. C. R. de Souza
    Getúlio J. A. Amaral
    Ronei M. de Moraes
    Applied Intelligence, 2024, 54 : 1545 - 1565
  • [3] Robust regression with application to symbolic interval data
    Fagundes, Roberta A. A.
    de Souza, Renata M. C. R.
    Cysneiros, Francisco Jose A.
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2013, 26 (01) : 564 - 573
  • [4] Symbolic interval-valued data analysis for time series based on auto-interval-regressive models
    Liang-Ching Lin
    Hsiang-Lin Chien
    Sangyeol Lee
    Statistical Methods & Applications, 2021, 30 : 295 - 315
  • [5] Symbolic interval-valued data analysis for time series based on auto-interval-regressive models
    Lin, Liang-Ching
    Chien, Hsiang-Lin
    Lee, Sangyeol
    STATISTICAL METHODS AND APPLICATIONS, 2021, 30 (01) : 295 - 315
  • [6] Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series
    de Carvalho, Miguel
    Martos, Gabriel
    JOURNAL OF FORECASTING, 2022, 41 (01) : 167 - 180
  • [7] Kohonen map-wise regression applied to interval data
    Souza, Leandro C.
    Pimentel, Bruno A.
    Filho, Telmo de M. Silva
    de Souza, Renata M. C. R.
    KNOWLEDGE-BASED SYSTEMS, 2021, 224
  • [8] A three-stage approach for modeling multiple time series applied to symbolic quartile data
    Reyes, Dailys M. A.
    de Souza, Renata M. C. R.
    de Oliveira, Adriano L., I
    EXPERT SYSTEMS WITH APPLICATIONS, 2022, 187
  • [9] Adaptive Hausdorff distances and dynamic clustering of symbolic interval data
    de Carvalho, FDT
    de Souza, RMCR
    Chavent, M
    Lechevallier, Y
    PATTERN RECOGNITION LETTERS, 2006, 27 (03) : 167 - 179
  • [10] 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
12345