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Unsupervised Feature Learning From Time-Series Data Using Linear Models
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DOI：10.1109/JIOT.2018.2845340.png)
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Abstract
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In the Internet of Things (IoT), heterogenous sensors generate time-series data with different properties. The problem of unsupervised feature learning from a time-series dataset poses two challenges. First, it is known that centroids obtained by clustering time-series with high overlap do not reflect their patterns, i.e., subsequence time-series clustering is meaningless. In this paper, we show that principal component analysis, sparse coding, and non-negative matrix factorization are also meaningless for the same task, and that the systematic approach to learning meaningful features from time-series is by using the shift-invariant versions of these algorithms. Second, by comparing their shift-invariant versions on different kinds of time-series for reconstruction, prediction and classification, we show that no one algorithm is best suited for all time-series. This comparison leads to a method for automatically selecting the suitable feature learning algorithm for a given time-series dataset based on its structural properties. Generality of the method and significance of the structural properties are examined using statistical tests. The method can be implemented as a simple logic circuit, convenient for embedding in IoT hardware.
Keywords:
Algorithm selection
clustering
convolution
Internet of Things (IoT)
matrix factorization
meaningfulness (MF)
shift-invariant
sparse coding
streaming data
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