Internet Protocol Television (IPTV) has had a significant impact on live TV content consumption in the past decade, as improvements in the broadband speed have allowed more data volume to be delivered. In addition to existing infrastructure, which is mostly based on the set top boxes, new content providers have emerged, utilizing newly developed proprietary streaming platforms. As the number of IPTV users grew, more volume and variety of data became available for analysis. By analyzing stored user actions, it is possible to create a multivariate time series that represents user behavior over time. The approach presented in the paper is based on multivariate time series generation from user data and determining the similarity between them. Time series are created for each user based on the proposed quantified action sets, grouped in the feature groups and summarized over time. The action sets and feature groups can be adjusted to a certain IPTV platform. The end result of the analysis is the similarity score matrix, generated by calculating the similarities of all users' time series, where the similarity measure calculation can be chosen arbitrarily.INDEX TERMS IPTV, time series analysis, data analysis, user behavior analysis, time series similarity, user profiling.