document: Algorithms for time series knowledge mining Temporal patterns composed of symbolic intervals are commonly formulated with Allen 's interval relations originating in temporal reasoning . This representation has severe disadvantages for knowledge discovery . The Time Series Knowledge Representation ( TSKR ) is a new hierarchical language for interval patterns expressing the temporal concepts of coincidence and partial order . We present effective and efficient mining algorithms for such patterns based on itemset techniques . A novel form of search space pruning effectively reduces the size of the mining result to ease interpretation and speed up the algorithms . On a real data set a concise set of TSKR patterns can explain the underlying temporal phenomena , whereas the patterns found with Allen 's relations are far more numerous yet only explain fragments of the data .
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