A fully automated periodicity detection in time series
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 the 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.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
A fully automated periodicity detection in time series
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 the state of the art algorithms.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 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.
assistant
 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.