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首页> 外文期刊>Expert systems with applications >Pm_(2.5) Concentration Prediction Using Hidden Semi-markov Model-based Times Series Data Mining
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Pm_(2.5) Concentration Prediction Using Hidden Semi-markov Model-based Times Series Data Mining

机译：基于隐半马尔可夫模型的时间序列数据挖掘进行Pm_（2.5）浓度预测

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摘要

In this paper, a novel framework and methodology based on hidden semi-Markov models (HSMMs) for high PM_(2.5) concentration value prediction is presented. Due to lack of explicit time structure and its short-term memory of past history, a standard hidden Markov model (HMM) has limited power in modeling the temporal structures of the prediction problems. To overcome the limitations of HMMs in prediction, we develop the HSMMs by adding the temporal structures into the HMMs and use them to predict the concentration levels of PM_(2.5). As a model-driven statistical learning method, HSMM assumes that both data and a mathematical model are available. In contrast to other data-driven statistical prediction models such as neural networks, a mathematical functional mapping between the parameters and the selected input variables can be established in HSMMs. In the proposed framework, states of HSMMs are used to represent the PM_(2.5) concentration levels. The model parameters are estimated through modified forward-backward training algorithm. The re-estimation formulae for model parameters are derived. The trained HSMMs can be used to predict high PM_(2.5) concentration levels. The validation of the proposed framework and methodology is carried out in real world applications: prediction of high PM_(2.5) concentrations at O'Hare airport in Chicago. The results show that the HSMMs provide accurate predictions of high PM_(2.5) concentration levels for the next 24 h.
机译：本文提出了一种基于隐式半马尔可夫模型（HSMM）的高PM_（2.5）浓度值预测的新颖框架和方法。由于缺乏明确的时间结构及其对过去历史的短期记忆，标准的隐马尔可夫模型（HMM）在建模预测问题的时间结构方面功能有限。为了克服HMM在预测中的局限性，我们通过将时间结构添加到HMM中来开发HSMM，并使用它们来预测PM_（2.5）的浓度水平。作为一种模型驱动的统计学习方法，HSMM假定数据和数学模型均可用。与其他数据驱动的统计预测模型（例如神经网络）相比，可以在HSMM中建立参数与所选输入变量之间的数学功能映射。在提出的框架中，HSMM的状态用于表示PM_（2.5）浓度水平。通过改进的前向后训练算法估计模型参数。推导了模型参数的重新估计公式。训练有素的HSMM可用于预测高PM_（2.5）浓度水平。对提议的框架和方法的验证是在实际应用中进行的：预测芝加哥奥黑尔机场的PM_（2.5）浓度高。结果表明，HSMMs可在接下来的24小时内准确预测高PM_（2.5）浓度水平。

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