Towards Symbolic Time Series Representation Improved by Kernel Density Estimators

Kloska, M., Rozinajova, V.

This paper deals with symbolic time series representation. It builds up on the popular mapping technique Symbolic Aggregate approXimation algorithm (SAX), which is extensively utilized in sequence classification, pattern mining, anomaly detection, time series indexing and other data mining tasks. However, the disadvantage of this method is, that it works reliably only for time series with Gaussian-like distribution. In our previous work (Kloska and Rozinajova, dwSAX, 2020) we have proposed an improvement of SAX, called dwSAX, which can deal with Gaussian as well as non-Gaussian data distribution. Recently we have made further progress in our solution – edwSAX. Our goal was to optimally cover the information space by means of sufficient alphabet utilization; and to satisfy lower bounding criterion as tight as possible. We describe here our approach, including evaluation on commonly employed tasks such as time series reconstruction error and Euclidean distance lower bounding with promising improvements over SAX.

Cite: Kloska, M., Rozinajova, V. Towards Symbolic Time Series Representation Improved by Kernel Density Estimators. Transactions on Large-Scale Data- and Knowledge-Centered Systems (2022). DOI: 10.1007/978-3-662-64553-6_2

KIniT basic research in 2021 and 2022 has also been supported by 


Matej Kloska
Research Engineer
Viera Rozinajová
Lead and Researcher