Consultas por similaridade e mineração de regras de associação: maximizando o conhecimento extraído de séries temporais

Abstract

A time series analysis presents challenges. There is a difficulty to manipulate the data by requiring a large computational cost, or even, by the difficulty of finding subsequences that have the same characteristics. However, this analysis is important for understanding the evolution of various phenomena such as climate change, changes in financial markets among others. This project proposed the development of a method for performing similarity queries in time series that have better performance and accuracy than the state-of-art and a method of mining association rules in series using similarity. The experiments performed have applied the proposed methods in real data sets, bringing relevant knowledge, indicating that both methods are suitable for analysis by similarity of one-dimensional and multidimensional time series.