Efficient time series data classification using sliding window technique based improved association rule mining with  enhanced support vector machine

Senthil, D.; Suseendran, G.

doi:10.14419/ijet.v7i2.33.13890

Cited by 8 publications

(8 citation statements)

References 13 publications

(13 reference statements)

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“…A técnica de janela deslizante é usada para fornecer uma representação mais compacta por meio de segmentação eficiente (SENTHIL; SUSEENDRAN, 2018). A técnica consiste em usar um determinado número n de dados que é chamado de janela para prever o valor do dia seguinte (HOTA; HANDA; SHRIVAS, 2017), o processo pode ser visto na Figura 4 com uma janela de tamanho 5.…”

Section: Figura 3 -Exemplos De Séries Temporaisunclassified

Redes Neurais Para Previsão De Vazões Do Rio Da Prata

MEDEIROS,

BATISTA

2022

Inteligência Artificial E Suas Aplicações Na Sociedade Moderna

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Inteligência Artificial e suas aplicações na sociedade moderna INTRODUÇÃOA previsão de resultados de partidas de futebol usando redes neurais é um fenômeno que vem sendo utilizada tanto em diferentes tipos de campeonatos de futebol quanto em diferentes linhas de pesquisa,como a utilização de Santos (2018) e Frozza (2021), seja simplesmente para a previsão matemática do vencedor, vice e outras posições do campeonato ou, a partir de aprendizado de máquina conforme a competição acontece e essas informações são armazenadas nos algoritmos, a rede neural é capaz de mostrar todas as estatísticas de equipes e jogadores com máxima precisão possível no final do torneio. Os trabalhos desenvolvidos com o tema deste projeto, a citar alguns: Peconick (2018), Santos (2019), Gamerman (2010) e Schneider (2018) contém diferentes empregabilidades, além de alguns desses trabalhos usarem torneios eliminatórios, com poucos dados para a rede neural artificial (RNA), e usarem modelos matemáticos e estatísticos para as previsões.Um modelo de liga, com mais jogos e mais times pode aprimorar a porcentagem de acerto da inteligência artificial (IA). Devemos considerar também que o futebol é um esporte complexo e que a diversos fatores são desconsiderados quando o quesito é uma previsão, pois há fatores que podem determinar o resultado de uma partida de maneira indireta, como condições climáticas da partida, formação dos times, jogadores machucados e entrosamento entre os jogadores dos times, dificultando assim o trabalho de previsão. O objetivo deste trabalho é aplicar as RNAs na temporada 2018-2019 da Premier League (PL) para que a IA, a partir dos resultados e das estatísticas de todas as 37 rodadas anteriores, consiga prever os resultados da rodada final com o máximo de precisão possível, bem como aumentar o conhecimentos dos discentes do curso de Ciência da Computação do Instituto Federal do Triângulo Mineiro -Campus Ituiutaba e também a comunidade acadêmica em geral sobre

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Section: Figura 3 -Exemplos De Séries Temporaisunclassified

Redes Neurais Para Previsão De Vazões Do Rio Da Prata

MEDEIROS,

BATISTA

2022

Inteligência Artificial E Suas Aplicações Na Sociedade Moderna

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“…There are existing systems such as Support Vector Machine (SVM) and Association Rule Mining (ARM) to implement time series data, but these systems have some issues with optimal rules discovery and segmentation on time series data, as well as lower accuracy and higher complexity (Senthil and Suseendran, 2018). To solve this issue Sliding Window Techniques (SWT) based systems were used.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To solve this issue Sliding Window Techniques (SWT) based systems were used. Two systems are mentioned in Senthil and Suseendran (2018), i.e. Improved ARM with Enhanced SVM (SWT-IARM with ESVM).…”

Section: Literature Reviewmentioning

confidence: 99%

Finding opportunity windows in time series data using the sliding window technique: The case of stock exchanges

2020

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Data have shapes, and human intelligence and perception have to classify the forms of data to understand and interpret them. This article uses a sliding window technique and the main aim is to answer two questions. Is there an opportunity window in time series of stock exchange index? The second question is how to find a way to use the opportunity window if there is one. The authors defined the term opportunity window as a window that is generated in the sliding window technique and can be used for forecasting. In analysis, the study determined the different frequencies and explained how to evaluate opportunity windows embedded using time series data for the S&P 500, the DJIA, and the Russell 2000 indices. As a result, for the S&P 500 the last days of the patterns 0111, 1100, 0011; for the DJIA the last days of the patterns 0101, 1001, 0011; and finally for the Russell 2000, the last days of the patterns 0100, 1001, 1100 are opportunity windows for prediction

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“…Senthil and Suseendran [5] has introduced an approach in the proposed research named Sliding Window Technique-based Improved ARM with Enhanced SVM (shortly represented as SWT-IARM with ESVM). In proposed system, preprocessing has performed in terms of using the Modified K-Means Clustering representing namely by MKMC.…”

Section: Literature Surveymentioning

confidence: 99%

“…This proposed work deals with data set which is typically very large in data size having huge amount of the attributes. In order to overcome issues pertaining to low accuracy and high computational complexity, hybrid method comprising Sliding Window Technique with Improved Association Rule Mining (SWT-IARM) and Enhanced Support Vector Machine (ESVM) [5] is adopted for the time series evaluation. This technique also does not deal with large size dataset.…”

Section: Introductionmentioning

confidence: 99%

Sequence Mining-Based Support Vector Machine with Decision Tree Approach for Efficient Time Series Data Classification

Senthil

Suseendran

2019

Data Management, Analytics and Innovation

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The growing demand for an efficient approach to classify time series data is bringing forth numerous research efforts in data mining field. Popularly known applications like business, medical and meteorology and so on, typically involves majority of data type in the form of time series. Hence, it is crucial to identify and scope out the potential of time series data owing to its importance on understanding the past trend as well as predicting about what would occur in future. To efficiently analyze the time series data, a system design based on Sliding Window Technique-Improved Association Rule Mining (SWT-IARM) with Enhanced Support Vector Machine (ESVM) has been largely adopted in the recent past. However, it does not provide a high accuracy for larger size of the dataset along with huge number of attributes. To solve this problem the proposed system designed a Sequence Mining algorithm-based Support Vector Machine with Decision Tree algorithm (SM-SVM with DT) for efficient time series analysis. In this proposed work, the larger size of the dataset is considered along with huge number of attributes. The preprocessing is performed using Kalman filtering. The hybrid segmentation method is proposed by combining a clustering technique and Particle Swarm Optimization (PSO) algorithm. Based on the sequence mining algorithm, the rule discovery is performed to reduce the computational complexity prominently by extracting the most frequent and important rules. In order to provide better time series classification results, the Support Vector Machine with Decision Tree (SVM-DT) method is utilized. Finally, the Pattern matching-based modified Spearmen's rank correlation coefficient technique is introduced to provide more similarity and classification results for the given larger time series dataset accurately. The experimental results shows that the pro-

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Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine

Cited by 8 publications

References 13 publications

Redes Neurais Para Previsão De Vazões Do Rio Da Prata

Redes Neurais Para Previsão De Vazões Do Rio Da Prata

Finding opportunity windows in time series data using the sliding window technique: The case of stock exchanges

Sequence Mining-Based Support Vector Machine with Decision Tree Approach for Efficient Time Series Data Classification

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