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Time series cluster kernel for learning similarities between multivariate time series with missing data

Permanent lenke
https://hdl.handle.net/10037/13578
DOI
https://doi.org/10.1016/j.patcog.2017.11.030
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article.pdf (908.0Kb)
Accepted manuscript version (PDF)
Dato
2017-12-06
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Forfatter
Mikalsen, Karl Øyvind; Bianchi, Filippo Maria; Soguero-Ruiz, Cristina; Jenssen, Robert
Sammendrag

Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or the time series contain missing data. In this paper, we address these challenges within the powerful context of kernel methods by proposing the robust time series cluster kernel (TCK). The approach taken leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with informative prior distributions. An ensemble learning approach is exploited to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel.

We evaluate the TCK on synthetic and real data and compare to other state-of-the-art techniques. The experimental results demonstrate that the TCK is robust to parameter choices, provides competitive results for MTS without missing data and outstanding results for missing data.

Beskrivelse
Accepted manuscript version. Published version available at https://doi.org/10.1016/j.patcog.2017.11.030. Accepted manuscript version, licensed CC BY-NC-ND 4.0.
Er en del av
The final version of this article is part of

Mikalsen, K.Ø. (2019). Advancing Unsupervised and Weakly Supervised Learning with Emphasis on Data-Driven Healthcare. (Doctoral thesis). http://hdl.handle.net/10037/14659.

Forlag
Elsevier
Sitering
Mikalsen, K.Ø., Bianchi, F.M., Soguero-Ruiz, C. & Jenssen, R. (2017). Time series cluster kernel for learning similarities between multivariate time series with missing data. Pattern Recognition, 76, 569-581. https://doi.org/10.1016/j.patcog.2017.11.030
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