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A time series retrieval tool for sub-series matching

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Abstract

The problem of retrieving time series similar to a specified query pattern has been recently addressed within the case based reasoning (CBR) literature. Providing a flexible and efficient way of dealing with such an issue is of paramount importance in many domains (e.g., medical), where the evolution of specific parameters is collected in the form of time series. In the past, we have developed a framework for retrieving time series, applying temporal abstractions. With respect to more classical (mathematical) approaches, our framework provides significant advantages. In particular, multi-level abstraction mechanisms and proper indexing techniques allow for flexible query issuing, and for efficient and interactive query answering. In this paper, we present an extension to such a framework, which aims to support sub-series matching as well. Indeed, sub-series retrieval may be crucial when the whole time series evolution is not of interest, while critical patterns to be searched for are only “local”. Moreover, sometimes the relative order of patterns, but not their precise location in time, may be known. Finally, an interactive search, at different abstraction levels, may be required by the decision maker. Our extended framework (which is currently being applied in haemodialysis, but is domain independent) deals with all these issues.

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Notes

  1. The result of abstracting (one or more) dummy symbol(s) is obviously domain dependent, and has to be defined by experts while setting up the system.

  2. Our indexing approach may resemble association rule mining with frequent pattern (FP) trees. Association rule mining is a well-known method for discovering interesting relations between variables in large databases. The FP-growth algorithm [39] has been proposed in this context to mine frequent itemsets, that will be the input for rule mining. FP-growth is an efficient and scalable method for mining the complete set of frequent patterns, using an extended prefix-tree structure for storing information named FP-tree. Despite the use of the tree structure, it is, however, clear that our approach is only loosely related to FP trees, since our goal (i.e., time series retrieval) is very different.

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Authors and Affiliations

  1. Dipartimento di Scienze e Innovazione Tecnologica, Computer Science Institute, Università del Piemonte Orientale, Viale Michel 11, Alessandria, Italy

    Alessio Bottrighi, Giorgio Leonardi, Stefania Montani, Luigi Portinale & Paolo Terenziani

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  1. Alessio Bottrighi
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  2. Giorgio Leonardi
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  3. Stefania Montani
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  4. Luigi Portinale
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  5. Paolo Terenziani
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Correspondence to Stefania Montani.

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Bottrighi, A., Leonardi, G., Montani, S. et al. A time series retrieval tool for sub-series matching. Appl Intell 43, 132–149 (2015). https://doi.org/10.1007/s10489-014-0628-8

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