Výsledky vyhledávání - "TIME SERIES CHARACTERIZATION"

Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models

Autor: Jack L. Follis, Dejian Lai

Publikováno v: Signals
Volume 1
Issue 1
Pages 3-46

Objective: To determine if there was a difference in the volatility characteristics of seizure and non-seizure onset channels in the intracranial electroencephalogram (EEG) in a patient with temporal lobe epilepsy. Methods: The half-life of volatilit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c99d60b92f315fe809012c90dded024
https://doi.org/10.3390/signals1010003

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Model-Free Characterization of Low-Noise, Nonlinear MTI Time Series

Autor: Refice, Alberto, Bovenga, Fabio, Pasquariello, Guido

Publikováno v: FRINGE 2021, Online, 31/05-04/06/2021
info:cnr-pdr/source/autori:Refice, Alberto; Bovenga, Fabio; Pasquariello, Guido/congresso_nome:FRINGE 2021/congresso_luogo:Online/congresso_data:31%2F05-04%2F06%2F2021/anno:2021/pagina_da:/pagina_a:/intervallo_pagine

Multi-temporal InSAR (MTI) displacement time series are usually characterized by the merit figure known as temporal phase coherence, which is the vector sum on the complex residues left after removing a model phase trend, which is imposed a priori. M

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=cnr_________::62df0fe9a1f8b8a44b60d50c7fd4528f
https://fringe2021.esa.int/iframe-agenda/index.html#day1622498400_room1_time_1_3

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Learning and distinguishing time series dynamics via ordinal patterns transition graphs

Autor: Raquel A. F. Mini, João B. Borges, Antonio A. F. Loureiro, Osvaldo A. Rosso, Alejandro C. Frery, Heitor S. Ramos

Strategies based on the extraction of measures from ordinal patterns transformation, such as probability distributions and transition graphs, have reached relevant advancements in distinguishing different time series dynamics. However, the reliabilit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::309b39b6f35dde028d715e39a7411201
https://www.sciencedirect.com/science/article/abs/pii/S0096300319305375

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The quest for sense: Physical phenomena classification in the Internet of Things

Autor: Borges, João, Ramos, Heitor, Mini, Raquel, Carneiro Viana, Aline, Loureiro, Antonio

Publikováno v: ISIoT 2019-1st International Workshop on Intelligent Systems for IoT
ISIoT 2019-1st International Workshop on Intelligent Systems for IoT, May 2019, Santorini, Greece

International audience; This paper investigates the precise identification of physical phenomena in the Internet of Things (IoT) context, which is one of the main challenges when dealing with the massive scale of IoT data. For this, we use informatio

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::135df3b24ee5305efacafc6ad40d4a36
https://hal.inria.fr/hal-02165145

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