Recurrent Auto-Encoder Model for Multidimensional Time Series Representation
Recurrent auto-encoder model can summarise sequential data through an encoder structure into a fixed-length vector and then reconstruct into its original sequential form through the decoder structure. The summarised information can be used to represent time series features. In this paper, we propose relaxing the dimensionality of the decoder output so that it performs partial reconstruction. The fixed-length vector can therefore represent features only in the selected dimensions. In addition, we propose using rolling fixed window approach to generate samples. The change of time series features over time can be summarised as a smooth trajectory path. The fixed-length vectors are further analysed through additional visualisation and unsupervised clustering techniques. This proposed method can be applied in large-scale industrial processes for sensors signal analysis purpose where clusters of the vector representations can be used to reflect the operating states of selected aspects of the industrial system.
 This paper proposes a strategy that is inspired by the recurrent auto-encoder model, such that clustering of multidimensional time series data can be performed based on the context vectors generated by the encoding process. Unfortunately, the paper in its current form is a bit thin on content. Main issues: No related works (such as those using RNN for time series analysis or clustering of time series data streams etc.) were described by the paper, no baselines were used in the comparison evaluations, and no settings/details were provided in the experiment section. As a result, it is quite difficult to judge the merits and novelty of the paper. Other issues: some contribution claims highlighted in the Discussion Section, i.e., Section 4, are arguable and should be further extended. For example, the authors claim that the proposed LSTM-based autoencoder networks can be natively scaled up to data with very high dimensionality. I would like the authors to explain it in more details or empirically demonstrate that, since a LSTM-based model could be computationally expensive. As another example, the authors claim that reducing the dimensionality of the output sequence is one of the main contributions of the paper. In this sense, further elaborations from that perspective would be very beneficial since some networks already employ such a mechanism. In short, the paper in its current form does not provide sufficient details for the reviewer to judge its merits and contributions.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Recurrent Auto-Encoder Model for Multidimensional Time Series Representation
Recurrent auto-encoder model can summarise sequential data through an encoder structure into a fixed-length vector and then reconstruct into its original sequential form through the decoder structure. The summarised information can be used to represent time series features. In this paper, we propose relaxing the dimensionality of the decoder output so that it performs partial reconstruction. The fixed-length vector can therefore represent features only in the selected dimensions. In addition, we propose using rolling fixed window approach to generate samples. The change of time series features over time can be summarised as a smooth trajectory path. The fixed-length vectors are further analysed through additional visualisation and unsupervised clustering techniques. This proposed method can be applied in large-scale industrial processes for sensors signal analysis purpose where clusters of the vector representations can be used to reflect the operating states of selected aspects of the industrial system.
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 proposes a strategy that is inspired by the recurrent auto-encoder model, such that clustering of multidimensional time series data can be performed based on the context vectors generated by the encoding process. Unfortunately, the paper in its current form is a bit thin on content. Main issues: No related works (such as those using RNN for time series analysis or clustering of time series data streams etc.) were described by the paper, no baselines were used in the comparison evaluations, and no settings/details were provided in the experiment section. As a result, it is quite difficult to judge the merits and novelty of the paper. Other issues: some contribution claims highlighted in the Discussion Section, i.e., Section 4, are arguable and should be further extended. For example, the authors claim that the proposed LSTM-based autoencoder networks can be natively scaled up to data with very high dimensionality. I would like the authors to explain it in more details or empirically demonstrate that, since a LSTM-based model could be computationally expensive. As another example, the authors claim that reducing the dimensionality of the output sequence is one of the main contributions of the paper. In this sense, further elaborations from that perspective would be very beneficial since some networks already employ such a mechanism. In short, the paper in its current form does not provide sufficient details for the reviewer to judge its merits and contributions.
assistant
 This paper proposes a strategy that is inspired by the recurrent auto-encoder model, such that clustering of multidimensional time series data can be performed based on the context vectors generated by the encoding process. Unfortunately, the paper in its current form is a bit thin on content. Main issues: No related works (such as those using RNN for time series analysis or clustering of time series data streams etc.) were described by the paper, no baselines were used in the comparison evaluations, and no settings/details were provided in the experiment section. As a result, it is quite difficult to judge the merits and novelty of the paper. Other issues: some contribution claims highlighted in the Discussion Section, i.e., Section 4, are arguable and should be further extended. For example, the authors claim that the proposed LSTM-based autoencoder networks can be natively scaled up to data with very high dimensionality. I would like the authors to explain it in more details or empirically demonstrate that, since a LSTM-based model could be computationally expensive. As another example, the authors claim that reducing the dimensionality of the output sequence is one of the main contributions of the paper. In this sense, further elaborations from that perspective would be very beneficial since some networks already employ such a mechanism. In short, the paper in its current form does not provide sufficient details for the reviewer to judge its merits and contributions.