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.
 The authors present a heuristic method to detect periodicity in time series. It extends a previous approach in dealing with noise and the setting of multiple periodicities. The topic does not match the scope of ICLR and would be better suited for a different venue. The method is demonstrated in a purely experimental fashion. However, without detailed inspection of the datasets it remains unclear in what cases the heuristics apply and where they fail. A more thorough analysis of the robustness of the algorithm is necessary, in particular a detailed presentation of failure cases.
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>



 The authors present a heuristic method to detect periodicity in time series. It extends a previous approach in dealing with noise and the setting of multiple periodicities. The topic does not match the scope of ICLR and would be better suited for a different venue. The method is demonstrated in a purely experimental fashion. However, without detailed inspection of the datasets it remains unclear in what cases the heuristics apply and where they fail. A more thorough analysis of the robustness of the algorithm is necessary, in particular a detailed presentation of failure cases.
assistant
 The authors present a heuristic method to detect periodicity in time series. It extends a previous approach in dealing with noise and the setting of multiple periodicities. The topic does not match the scope of ICLR and would be better suited for a different venue. The method is demonstrated in a purely experimental fashion. However, without detailed inspection of the datasets it remains unclear in what cases the heuristics apply and where they fail. A more thorough analysis of the robustness of the algorithm is necessary, in particular a detailed presentation of failure cases.