Abstract
Many algorithms for data mining or indexing time series data do not operate directly on the raw data, but instead they use alternative representations that include transforms, quantization, approximation, and multi-resolution abstractions. Choosing the best representation and abstraction level for a given task/dataset is arguably the most critical step in time series data mining. In this work, we investigate the problem of discovering the natural intrinsic representation model, dimensionality and alphabet cardinality of a time series. The ability to automatically discover these intrinsic features has implications beyond selecting the best parameters for particular algorithms, as characterizing data in such a manner is useful in its own right and an important sub-routine in algorithms for classification, clustering and outlier discovery. We will frame the discovery of these intrinsic features in the Minimal Description Length framework. Extensive empirical tests show that our method is simpler, more general and more accurate than previous methods, and has the important advantage of being essentially parameter-free.
Highlights
Most algorithms for indexing or mining time series data operate on higher-level representations of the data, which include transforms, quantization, approximations and multi-resolution approaches
Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), Adaptive Piecewise Constant Approximation (APCA) and Piecewise Linear Approximation (PLA) are models that all have their advocates for various data mining tasks and each has been used extensively (Ding et al 2008)
The question of choosing the best abstraction level and/or representation of the data for a given task/dataset still remains. We investigate this problem by discovering the natural intrinsic model, dimensionality and cardinality of a time series
Summary
Most algorithms for indexing or mining time series data operate on higher-level representations of the data, which include transforms, quantization, approximations and multi-resolution approaches. Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), Adaptive Piecewise Constant Approximation (APCA) and Piecewise Linear Approximation (PLA) are models that all have their advocates for various data mining tasks and each has been used extensively (Ding et al 2008). The question of choosing the best abstraction level and/or representation of the data for a given task/dataset still remains. MDL is the cornerstone of many bioinformatics algorithms (Evans et al 2007; Rissanen 1989), and has had some impact in data mining, it is arguably underutilized in time series data mining (Jonyer et al 2004; Papadimitriou et al 2005)
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