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Rule Evolution Approach for Mining Multivariate Time Series Data

In the last few decades, due to the massive explosion of data over the world, time series data mining has attracted numerous researches and applications. Most of these works only focus on time series of a single attribute or univariate time series data. However, in many real world problems, data arrive as one or many series of multiple attributes, i.e., they are multivariate time series data. Because they contain multiple attributes, multivariate time series data promise to provide more intrinsic correlations among them, from which more important information can be gathered and extracted. In this paper, we present a novel approach to model and predict the behaviors of multivariate time series data based on a rule evolution methodology. Our approach is divided into distinct steps, each of which can be accomplished by several machine learning techniques. This makes our system highly flexible, configurable and extendable. Experiments are also conducted on real S&P 500 stock data to examine the effectiveness and reliability of our approach. Empirical results demonstrate that our system has substantial estimation reliability and prediction accuracy.