Time series analysis with dynamic law exploration
This work addresses time series analysis for researchers, but appears incremental as it builds on existing methods without clear novel breakthroughs.
The paper tackles the problem of identifying dynamic laws governing time series evolution by providing finite difference and differential equation representations, and studies the compression performance of linear laws on sound data, achieving unspecified results.
In this paper we examine, how the dynamic laws governing the time evolution of a time series can be identified. We give a finite difference equation as well as a differential equation representation for that. We also study, how the required symmetries, like time reversal can be imposed on the laws. We study the compression performance of linear laws on sound data.