This paper presents a method to autonomously find periodicities in a signal. It is based on the same idea of using Fourier Transform and autocorrelation function presented in Vlachos et al. (2005). While showing interesting results this method does not perform well on noisy signals or signals with multiple periodicities. Thus, our method adds several new extra steps (hints clustering, filtering and detrending) to fix these issues. Experimental results show that the proposed method outperforms state of the art algorithms.
This paper focuses on the extraction of the (multi) periodicities from a signal. The paper describes the conventional method based on the Fourier transformation and/or autocorrelation methods, and proposed method, which first detects a distribution of spectral leakages, and prune the periodicity hints by using a clustering algorithm. The proposed method is also extended to deal with multi-periodicities. The effectiveness of the proposed method is shown with the controlled simulation data and several real data. This paper is well written (note it is over 8 pages though), but it is not learning-based approach, and would not best fit to major ICLR interests.
Comments:
- The abstract needs to be more self-consistent without referring the citation for a brief explanation. Also it should have more detailed experimental discussions.
- Algorithm 1 needs some refinement (too code-like, although it is understandable). For example, several methods (nextBinValue and append) would be better to be replaced with other (human readable) expressions.