Classification of time series generation processes using experimental tools: a survey and proposal of an automatic and systematic approach

Ishii, Renato Porfírio; Rios, Ricardo Araújo; Mello, Rodrigo Fernandes de

doi:10.1504/ijcse.2011.043919

Cited by 9 publications

(21 citation statements)

References 29 publications

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“…Os fluxos adotados fornecem características de aplicações frequentemente encontradas em contextos reais, tais como alto nível de ruído, caoticidade, não estacionariedade, amostragem de alta frequência e misturas de diferentes fontes (Ishii et al, 2011).…”

Section: Comparação Entre Algoritmosunclassified

“…Os experimentos foram realizados a partir da geração de sequências sintéticas de dados com ruídos. Cada sequência sintética é composta de um componente determinístico, que representa o modelo ideal para o fenômeno observado, e um componente com ruídos, que representa interferências indesejadas no processo de coleta dos dados (Ishii et al, 2011). Por exemplo, para obter a sequência senoide apresentada na Figura 4.10, executou-se, na linguagem R, o comando sin(seq(0,20, length = 400)) + rnorm(400), que utiliza a função seno como o modelo ideal para o fenômeno em estudo e a distribuição de probabilidades Normal N (µ = 0, σ 2 = 1) para representar os ruídos identicamente distribuídos e independentes da sequência.…”

Section: Experimentosunclassified

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Adaptação de viés indutivo de algoritmos de agrupamento de fluxos de dados

Albertini¹

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Section: Comparação Entre Algoritmosunclassified

Section: Experimentosunclassified

Adaptação de viés indutivo de algoritmos de agrupamento de fluxos de dados

Albertini¹

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“…Séries estocásticas, por sua vez, dependem de algum processo aleatório, além de poderem depender de seu histórico de observações. Técnicas estatísticas são mais apropriadas para uso em séries estocásticas (Shumway e Stoffer, 2010;Ishii et al, 2011).…”

Section: Características Do Sinal Do Eegunclassified

“…No caso de uma série estocástica, deve-se ainda considerar se a série é linear, formada por uma combinação linear de valores do passado e ruídos, ou não-linear, formada por combinações não lineares desses valores e que são mais difíceis de modelar. Além disso, a série temporal ainda pode ser classificada como estacionária, evoluindo no tempo sobre uma média e variância constante e não estacionária quando a série não satisfaz essas condições, apresentando comportamentos evolucionários com a média dos valores mudando quando novos valores são considerados (Morettin e Toloi, 2004;Ishii et al, 2011).…”

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Conjuntos K de redes neurais e sua aplicação na classificação de imagética motora

Piazentin¹

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“…By knowing these components, one can assess the influence of important features that permit improving the accuracy of the estimated models. Among those features are stationarity, linearity, and stochasticity (Ishii et al, 2011).…”

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confidence: 99%

Improving time series modeling by decomposing and analysing stochastic and deterministic influences

Rios¹

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First of all, I would like to offer my gratitude to God, who has been blessing me every single day. I also wish to express my sincere gratitude to my advisor, professor Rodrigo Mello. During the last years, he was more than a formal advisor, helping me to understand new concepts and working hard to produce this work. Thank you very much, Rodrigo, for showing me the importance of researching without having numbers as parameter. For sure, our collaboration and friendship will never end. Well, I was impatient to start talking about my wife and my daughter. Tati, you are my strength, my motivation, and my inspiration. Thank you very much for your patience and love. You are present in every page of this thesis. Thank you very much for making my days and trips funnier. Mari, my little girl, you came up a few months ago, but I cannot even remeber my life before you. You suddenly became the main reason for everything. Your tender smile moves me and makes me forget any tough situation. I am deeply grateful to my father Robério, my mother Josinha, my brothers Guga, Joyce (Guga's wife and my stepsister :]), and Bebel. You are the greatest models for me. I am pretty sure you are always by my side, regardless the situation I know I can count on you. I love you so much. My grandfather Alcides and my grandmother Isabel, you started all this mess when you left behind a quiet life in the countryside to live in a bigger city and give better conditions to your children. I am forever grateful to you. I would like to thank my new family Valdeci, Dilzete, Tiago, Eva e Rodrigo for being always there sharing amazing moments. Professor Michall Small, thank you very much for your help and support during my stay in Hong Kong. You allowed me to develop a new research line and to try out one of the most incredible experience of my life. The memories from HK will never leave my head. Thank you also Ruoxi Xiang, Xiaoke Xu, and Liu Xiaofan for our chats that helped me during the adaptation process and made me to learn and respect your culture even more. I also thank Professor Lael Parrott for your help while I was in Montreal, for your dedi ication during our meetings and all support after my return to Brazil. I am also thankful to Gillaume Latombe, Phillipe Girard, Clément Chion, Caroline Von Schilling, Rodolphe Coulon, and Patrick Verhaar. You guys made my workdays more pleasant with our coffee breaks. My dear friends Pedro, Taynara, Sophie, Leo, Aninha, Lucas, Natalia, Théo, you helped me a lot during the process of settling down. You guys were/are our family in Montreal. I have learnt a lot from you and I will never forget every moment shared with you (Does this call mean something?). Afshin, Mme Yanick and Mme Marie, «je vous remercie beaucoupe pour m'aider avec le frances!» I will never forget you. Mr. Richard Lancing, your blessing messages every sunday morning gave me peace and strength. Thank you very much.

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Classification of time series generation processes using experimental tools: a survey and proposal of an automatic and systematic approach

Cited by 9 publications

References 29 publications

Adaptação de viés indutivo de algoritmos de agrupamento de fluxos de dados

Adaptação de viés indutivo de algoritmos de agrupamento de fluxos de dados

Conjuntos K de redes neurais e sua aplicação na classificação de imagética motora

Improving time series modeling by decomposing and analysing stochastic and deterministic influences

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