Detecting Change in Seasonal Pattern via Autoencoder and Temporal Regularization
Change-point detection problem consists of discovering abrupt property changes in the generation process of time-series. Most state-of-the-art models are optimizing the power of a kernel two-sample test, with only a few assumptions on the distribution of the data. Unfortunately, because they presume the samples are distributed i.i.d, they are not able to use information about the seasonality of a time-series. In this paper, we present a novel approach - ATR-CSPD allowing the detection of changes in the seasonal pattern of a time-series. Our method uses an autoencoder together with a temporal regularization, to learn the pattern of each seasonal cycle. Using low dimensional representation of the seasonal patterns, it is possible to accurately and efficiently estimate the existence of a change point using a clustering algorithm. Through experiments on artificial and real-world data sets, we demonstrate the usefulness of the proposed method for several applications.
I am quite disappointed with the presentation and technical quality of the paper. There are numerous grammatical errors that make the reading unpleasant. The mathematical notations are also inconsistent throughout different places in the paper. The extensive literature of modelling time series with seasonality trends, both in the statistics and the machine learning community, is severely under-represented in the motivations and related works. Models like SARIMA have no mention in the paper. The temporal regularization imposed in section 3.2, coupled with an autoencoder, does not seem very different from the state-space models and their more complex and recent variants that use multi-layered networks (a google search will provide plenty references). The experiments, with many of the useful baselines missing, are equally unimpressive.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Detecting Change in Seasonal Pattern via Autoencoder and Temporal Regularization
Change-point detection problem consists of discovering abrupt property changes in the generation process of time-series. Most state-of-the-art models are optimizing the power of a kernel two-sample test, with only a few assumptions on the distribution of the data. Unfortunately, because they presume the samples are distributed i.i.d, they are not able to use information about the seasonality of a time-series. In this paper, we present a novel approach - ATR-CSPD allowing the detection of changes in the seasonal pattern of a time-series. Our method uses an autoencoder together with a temporal regularization, to learn the pattern of each seasonal cycle. Using low dimensional representation of the seasonal patterns, it is possible to accurately and efficiently estimate the existence of a change point using a clustering algorithm. Through experiments on artificial and real-world data sets, we demonstrate the usefulness of the proposed method for several applications.
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>



I am quite disappointed with the presentation and technical quality of the paper. There are numerous grammatical errors that make the reading unpleasant. The mathematical notations are also inconsistent throughout different places in the paper. The extensive literature of modelling time series with seasonality trends, both in the statistics and the machine learning community, is severely under-represented in the motivations and related works. Models like SARIMA have no mention in the paper. The temporal regularization imposed in section 3.2, coupled with an autoencoder, does not seem very different from the state-space models and their more complex and recent variants that use multi-layered networks (a google search will provide plenty references). The experiments, with many of the useful baselines missing, are equally unimpressive.
assistant
I am quite disappointed with the presentation and technical quality of the paper. There are numerous grammatical errors that make the reading unpleasant. The mathematical notations are also inconsistent throughout different places in the paper. The extensive literature of modelling time series with seasonality trends, both in the statistics and the machine learning community, is severely under-represented in the motivations and related works. Models like SARIMA have no mention in the paper. The temporal regularization imposed in section 3.2, coupled with an autoencoder, does not seem very different from the state-space models and their more complex and recent variants that use multi-layered networks (a google search will provide plenty references). The experiments, with many of the useful baselines missing, are equally unimpressive.