Pyramid Recurrent Neural Networks for Multi-Scale Change-Point Detection
Many real-world time series, such as in activity recognition, finance, or climate science, have changepoints where the system*s structure or parameters change. Detecting changes is important as they may indicate critical events. However, existing methods for changepoint detection face challenges when (1) the patterns of change cannot be modeled using simple and predefined metrics, and (2) changes can occur gradually, at multiple time-scales. To address this, we show how changepoint detection can be treated as a supervised learning problem, and propose a new deep neural network architecture that can efficiently identify both abrupt and gradual changes at multiple scales. Our proposed method, pyramid recurrent neural network (PRNN), is designed to be scale-invariant, by incorporating wavelets and pyramid analysis techniques from multi-scale signal processing. Through experiments on synthetic and real-world datasets, we show that PRNN can detect abrupt and gradual changes with higher accuracy than the state of the art and can extrapolate to detect changepoints at novel timescales that have not been seen in training.
 The paper presents an interesting approach to change point detection. I agree we need more general model to capture the change. However, unfortunately, they did not place the contribution correctly with respect to existing literature. The comments for prior work seem to be highly biased. For instance, in Section 2, "these methods either have unrealistic assumptions, such as defining changes as a large difference in covariance matrix". I would like to comment that, covariance change can capture a large number of changes in real applications and these are not unrealistic assumptions. The "pyramid" recurrent neural network seems to be a extension of RNN using the idea of multi-scale structure. Could be interesting. The paper gives too much emphasis on the "merit" of the neural networks on capturing the change patterns. However, there is a very important aspect been ignored or hiding: in order to train neural networks to capture anomaly patterns, since neural networks are highly over-parameterized model, usually there won*t be a large number of samples for anomalies. Therefore, in many situations, it is simply unpractical to train neural networks to capture post-change samples. There is a large body of literature on change point detection in statistics etc. (the author mentioned one, Chen and Zhang 2015, more over, the comment that "they can only detect abrupt change" is wrong, the method is quite general). The paper fails to have any comparison with existing methods. For instance, how does the proposed method compare with hoteling T-square statistic, or CUSUM statistic, or generalize likelihood ratio statistic, or MMD statistic (non-parametric approach)? Without any comparison, it does not make sense to claim proposed method is superior.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Pyramid Recurrent Neural Networks for Multi-Scale Change-Point Detection
Many real-world time series, such as in activity recognition, finance, or climate science, have changepoints where the system*s structure or parameters change. Detecting changes is important as they may indicate critical events. However, existing methods for changepoint detection face challenges when (1) the patterns of change cannot be modeled using simple and predefined metrics, and (2) changes can occur gradually, at multiple time-scales. To address this, we show how changepoint detection can be treated as a supervised learning problem, and propose a new deep neural network architecture that can efficiently identify both abrupt and gradual changes at multiple scales. Our proposed method, pyramid recurrent neural network (PRNN), is designed to be scale-invariant, by incorporating wavelets and pyramid analysis techniques from multi-scale signal processing. Through experiments on synthetic and real-world datasets, we show that PRNN can detect abrupt and gradual changes with higher accuracy than the state of the art and can extrapolate to detect changepoints at novel timescales that have not been seen in training.
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 paper presents an interesting approach to change point detection. I agree we need more general model to capture the change. However, unfortunately, they did not place the contribution correctly with respect to existing literature. The comments for prior work seem to be highly biased. For instance, in Section 2, "these methods either have unrealistic assumptions, such as defining changes as a large difference in covariance matrix". I would like to comment that, covariance change can capture a large number of changes in real applications and these are not unrealistic assumptions. The "pyramid" recurrent neural network seems to be a extension of RNN using the idea of multi-scale structure. Could be interesting. The paper gives too much emphasis on the "merit" of the neural networks on capturing the change patterns. However, there is a very important aspect been ignored or hiding: in order to train neural networks to capture anomaly patterns, since neural networks are highly over-parameterized model, usually there won*t be a large number of samples for anomalies. Therefore, in many situations, it is simply unpractical to train neural networks to capture post-change samples. There is a large body of literature on change point detection in statistics etc. (the author mentioned one, Chen and Zhang 2015, more over, the comment that "they can only detect abrupt change" is wrong, the method is quite general). The paper fails to have any comparison with existing methods. For instance, how does the proposed method compare with hoteling T-square statistic, or CUSUM statistic, or generalize likelihood ratio statistic, or MMD statistic (non-parametric approach)? Without any comparison, it does not make sense to claim proposed method is superior.
assistant
 The paper presents an interesting approach to change point detection. I agree we need more general model to capture the change. However, unfortunately, they did not place the contribution correctly with respect to existing literature. The comments for prior work seem to be highly biased. For instance, in Section 2, "these methods either have unrealistic assumptions, such as defining changes as a large difference in covariance matrix". I would like to comment that, covariance change can capture a large number of changes in real applications and these are not unrealistic assumptions. The "pyramid" recurrent neural network seems to be a extension of RNN using the idea of multi-scale structure. Could be interesting. The paper gives too much emphasis on the "merit" of the neural networks on capturing the change patterns. However, there is a very important aspect been ignored or hiding: in order to train neural networks to capture anomaly patterns, since neural networks are highly over-parameterized model, usually there won*t be a large number of samples for anomalies. Therefore, in many situations, it is simply unpractical to train neural networks to capture post-change samples. There is a large body of literature on change point detection in statistics etc. (the author mentioned one, Chen and Zhang 2015, more over, the comment that "they can only detect abrupt change" is wrong, the method is quite general). The paper fails to have any comparison with existing methods. For instance, how does the proposed method compare with hoteling T-square statistic, or CUSUM statistic, or generalize likelihood ratio statistic, or MMD statistic (non-parametric approach)? Without any comparison, it does not make sense to claim proposed method is superior.