Classification of Time-Series Data using ptSTL

Date

2020-01-01

Author

Ergurtuna, Mert
Aydın Göl, Ebru

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this work, the goal is to find properties to classify time series data. These properties are expressed using past time Signal Temporal Logic (ptSTL). First, we extend monotonicity properties for signals with timed labels to signals with a single label with the purpose of optimizing parameters of template ptSTL formulas efficiently. This method optimizes a monotone criteria while keeping the complementary criteria (i.e. error) under a given bound. Then by iteratively combining optimized formulas, a classifier is generated for the time series data. Lastly, proposed method is illustrated on a case study.

URI

https://hdl.handle.net/11511/94019

Conference Name

28th Signal Processing and Communications Applications Conference (SIU)

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

M. Ergurtuna and E. Aydın Göl, “Classification of Time-Series Data using ptSTL,” presented at the 28th Signal Processing and Communications Applications Conference (SIU), ELECTR NETWORK, 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/94019.