Research And Application Of Financial Multivariate Time Series Mining Methods

Time series includes two kinds of data: univariate time series and multivariate time series. There are many researches on univariate time series mining, whose mature theory and methods have been proposed. However, there are a few on multivariate time series mining, since the data structure of multivariate time series is more complex than that of univariate time series. It is of great significance to investigate in multivariate time series mining, because the data of multivariate time series are widely collected from many research and application fields, such as Finance, Monitoring, medicine etc.Similarity measure is a key technology of time series mining, whose existing methods are not available for the analysis of small-scale multivariate time series. Clustering provides an important support for stationarity analysis, which is the preprocessing of parametric modeling, but the accuracy is still low. It is easy to carry out the transformation from unvaraite time series to multivariate time series with portfolio. though clustering provides a useful way for asset selection, but its ability remains to further investigation. The existing methods of similarity search are not suitable for high frequency financial data, which is a kind of non-interval time series.The framework of this thesis is based on time series similarity analysis. It first engages in the researches of the similarity analysis for small-scale multivariate time series, and then studies the stationarity analysis and asset selection of portfolio, finally deals with the similarity search of high frequency financial data. Except for some researches on univariate time series as the basic study, this thesis also presents much more important theoretical and practical significance. The majority of our contributions can be summarized as following:1. The data structure of multivariate time series is deeply studied. Furthermore, the three-dimension curved surface is used to describe the multivariate time series, which is a useful method than can capture the shape character of multivariate time series; 2. A similarity analysis method based on points distribution is presented for small-scale multivariate times series, which can effectively capture the data character of multivariate time series;3. Clustering method is used to classify time series for stationarity analysis and a nonlinear transformation theory is presented. The combination method is better than the existing methods for stationarity analysis;4. A new way of asset selection is presented for portfolio, which is based on multiple binary function and time series clustering;5. The multiple nonlinear trend of autocorrelation function is presented for similarity search of high frequency financial data, and experiments about trend prediction of high frequency financial data have been done.