This paper addresses the issue of analyzing time series with temporal association rule mining techniques. Since originally association rule mining was developed for the analysis of transactional data, as it occurs for instance in market basket analysis, algorithms and time series have to be adapted in order to apply these techniques gainfully to the analysis of time series in general. Continuous time series of different origins can be discretized in order to mine several temporal association rules, what reveals interesting coherences in one and between pairs of time series. Depending on the domain, the knowledge about these coherences can be used for several purposes, e.g. for the prediction of future values of time series. We present a short review on different standard and temporal association rule mining approaches and on approaches that apply association rule mining to time series analysis. In addition to that, we explain in detail how some of the most interesting kinds of temporal association rules can be mined from continuous time series and present an prototype implementation. We demonstrate and evaluate our implementation on two large datasets containing river level measurement and stock data.
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