Mining time series is a machine learning subfield that focuses on a particular data structure, where variables are measured over (short or long) periods of time. In this thesis we focus on... Show moreMining time series is a machine learning subfield that focuses on a particular data structure, where variables are measured over (short or long) periods of time. In this thesis we focus on multivariate time series, with multiple vari- ables measured over the same period of time. In most cases, such variables are collected at different sampling rates. When combined, these variables can be explored with machine learning methods for multiple purposes.Firstly, we consider the possibility of unsupervised learning. In this case, we propose a pattern recognition method that discovers subsets of variables that show consistent behavior in a number of shared time segments. Fur- thermore, when in a supervised setting, given a dependent variable (target),we propose a method that aggregates independent variables into meaningful features.Additionally to the methods above, we provide two tools in the form of Software as a Service, where users without programming background can intuitively follow the learning and testing methodologies for both methods.Finally, we present an applied study of machine learning to improve speed skating athletes performance. Here, we make a deep analysis of historical data, in order to help optimize performance results. Show less
