Finding structure in multivariate time series

The scope of scientific data collection in modern projects such as the human genomeproject has made it effectively impossible for careful by-hand analyses of such data to becarried out. Simultaneously, the increase in computer power raises the possibility of replacinghuman scrutiny with computer systems that could effectively sort and filter copious data,presenting only the most salient features to researchers. This thesis details a method forcombining a generalized version of the classical statistical method known as canonical correlationanalysis, that possesses good computational properties, with the more recently developedmultitaper spectral estimators. The developed method allows researchers to combinedata from multiple experiments to generate more accurate spectral decompositions of theunderlying processes involved while also giving researchers a sensitive method for finding thelinks between variables in the data sets. The only limitation is that the data to be analyzedmust be homogeneous in certain specific ways (for example, it must contain no pronouncedtrends).