Prediction of ambient carbon monoxide concentration using nonlinear time series analysis technique

A.B. Chelani; S. Devotta

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Prediction of ambient carbon monoxide concentration using nonlinear time series analysis technique

机译：使用非线性时间序列分析技术预测环境一氧化碳浓度

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摘要

This study evaluates the potential of nonlinear time series analysis based methods in predicting the carbon monoxide concentration in an urban area. To establish the functional relationship between current and future observations, two models based on local approximations and neural network approximations are used. To compare the performance of the models, an autoregressive integrated moving average model is also applied. The multi-step forecasting capabilities of the models are evaluated.

机译：这项研究评估了基于非线性时间序列分析的方法在预测城市地区一氧化碳浓度方面的潜力。为了建立当前和未来观测值之间的函数关系，使用了基于局部逼近和神经网络逼近的两个模型。为了比较模型的性能，还应用了自回归综合移动平均模型。评估了模型的多步预测功能。

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来源
《Transportation Research》 |2007年第8期|596-600|共5页
作者
A.B. Chelani; S. Devotta;
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作者单位

National Environmental Engineering Research Institute, Nehru Marg, Nagpur, India;

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原文格式 PDF
正文语种 eng
中图分类交通运输;
关键词
time series forecasting; nonlinear dynamics; local approximations; neural networks; prediction horizon;

机译：时间序列预测;非线性动力学;局部近似神经网络;预测范围;

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Prediction of ambient carbon monoxide concentration using nonlinear time series analysis technique

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