用于 pm2.5 预报的自动归因深度时空卷积网络 (TCN) 模型

K. Krishna, Rani Samal
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引用次数: 0

摘要

缺失值的数据估算是数据工程(如空气质量建模)的关键问题之一。处理缺失的污染物值具有挑战性,因为它们是在不规则和不同的时间收集的。准确估计这些缺失值对于空气污染预测任务至关重要。有效的预测是空气质量建模的一个重要组成部分,有助于建立一个强大的预警系统。本研究开发了一种神经网络模型,即带有估算块(TCN-I)的时序卷积网络(TCN),可同时执行数据估算和预测任务。由于污染传感器数据存在不同类型的缺失值,且缺失原因各不相同,因此本研究尝试使用 TCN 对这些缺失值进行估算,并在单一模型中执行预测任务。结果证明,TCN-I 模型优于基线模型。
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Auto imputation enabled deep Temporal Convolutional Network (TCN) model for pm2.5 forecasting
Data imputation of missing values is one of the critical issues for data engineering, such as air quality modeling. It is challenging to handle missing pollutant values because they are collected at irregular and different times. Accurate estimation of those missing values is critical for the air pollution prediction task. Effective forecasting is a significant part of air quality modeling for a robust early warning system. This study developed a neural network model, a Temporal Convolutional Network (TCN) with an imputation block (TCN-I), to simultaneously perform data imputation and forecasting tasks. As pollution sensor data suffer from different types of missing values whose causes are varied, TCN is attempted to impute those missing values in this study and perform prediction tasks in a single model. The results prove that the TCN-I model outperforms the baseline models.
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