Automatically Inferring Data Quality for Spatiotemporal Forecasting
Spatiotemporal forecasting has become an increasingly important prediction task in machine learning and statistics due to its vast applications, such as climate modeling, traffic prediction, video caching predictions, and so on. While numerous studies have been conducted, most existing works assume that the data from different sources or across different locations are equally reliable. Due to cost, accessibility, or other factors, it is inevitable that the data quality could vary, which introduces significant biases into the model and leads to unreliable prediction results. The problem could be exacerbated in black-box prediction models, such as deep neural networks. In this paper, we propose a novel solution that can automatically infer data quality levels of different sources through local variations of spatiotemporal signals without explicit labels. Furthermore, we integrate the estimate of data quality level with graph convolutional networks to exploit their efficient structures. We evaluate our proposed method on forecasting temperatures in Los Angeles.
 Summary of the reviews: Pros: • A novel way to evaluate the quality of heterogeneous data sources. • An interesting way to put the data quality measurement as a regularization term in the objective function. Cons: • Since the data quality is a function of local variation, it is unclear about the advantage of the proposed data quality regularization versus using a simple moving average regularization or local smoothness regularization. • It is unclear about the advantage of using the Multi-layer graph convolutional networks versus two naïve settings for graph construction + simple LSTM, see detailed comments D1 Detailed comments: D1: Compared to the proposed approaches, there are two alternative naïve ways: 1) Instead of construct the similarity graph with Gaussian kernel and associated each vertex with different types of time-series, we can also construct one unified similarity graph that is a weighted combination of different types of data sources and then apply traditional LSTM; 2) During the GCN phases, one can apply type-aware random walk as an extension to the deep walk that can only handle a single source of data. It is unclear about the advantage of using the Multi-layer graph convolutional networks versus these two naïve settings. Either some discussions or empirical comparisons would be a plus.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Automatically Inferring Data Quality for Spatiotemporal Forecasting
Spatiotemporal forecasting has become an increasingly important prediction task in machine learning and statistics due to its vast applications, such as climate modeling, traffic prediction, video caching predictions, and so on. While numerous studies have been conducted, most existing works assume that the data from different sources or across different locations are equally reliable. Due to cost, accessibility, or other factors, it is inevitable that the data quality could vary, which introduces significant biases into the model and leads to unreliable prediction results. The problem could be exacerbated in black-box prediction models, such as deep neural networks. In this paper, we propose a novel solution that can automatically infer data quality levels of different sources through local variations of spatiotemporal signals without explicit labels. Furthermore, we integrate the estimate of data quality level with graph convolutional networks to exploit their efficient structures. We evaluate our proposed method on forecasting temperatures in Los Angeles.
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>



 Summary of the reviews: Pros: • A novel way to evaluate the quality of heterogeneous data sources. • An interesting way to put the data quality measurement as a regularization term in the objective function. Cons: • Since the data quality is a function of local variation, it is unclear about the advantage of the proposed data quality regularization versus using a simple moving average regularization or local smoothness regularization. • It is unclear about the advantage of using the Multi-layer graph convolutional networks versus two naïve settings for graph construction + simple LSTM, see detailed comments D1 Detailed comments: D1: Compared to the proposed approaches, there are two alternative naïve ways: 1) Instead of construct the similarity graph with Gaussian kernel and associated each vertex with different types of time-series, we can also construct one unified similarity graph that is a weighted combination of different types of data sources and then apply traditional LSTM; 2) During the GCN phases, one can apply type-aware random walk as an extension to the deep walk that can only handle a single source of data. It is unclear about the advantage of using the Multi-layer graph convolutional networks versus these two naïve settings. Either some discussions or empirical comparisons would be a plus.
assistant
 Summary of the reviews: Pros: • A novel way to evaluate the quality of heterogeneous data sources. • An interesting way to put the data quality measurement as a regularization term in the objective function. Cons: • Since the data quality is a function of local variation, it is unclear about the advantage of the proposed data quality regularization versus using a simple moving average regularization or local smoothness regularization. • It is unclear about the advantage of using the Multi-layer graph convolutional networks versus two naïve settings for graph construction + simple LSTM, see detailed comments D1 Detailed comments: D1: Compared to the proposed approaches, there are two alternative naïve ways: 1) Instead of construct the similarity graph with Gaussian kernel and associated each vertex with different types of time-series, we can also construct one unified similarity graph that is a weighted combination of different types of data sources and then apply traditional LSTM; 2) During the GCN phases, one can apply type-aware random walk as an extension to the deep walk that can only handle a single source of data. It is unclear about the advantage of using the Multi-layer graph convolutional networks versus these two naïve settings. Either some discussions or empirical comparisons would be a plus.