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Time series prediction of CO2, TVOC and HCHO based on machine learning at different sampling points

机译：基于不同采样点机器学习的CO2，TVOC和HCHO时间序列预测

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This paper presents novel findings about the prediction of TVOC and HCHO using the machine learning approach. Continuous measurements of CO2, TVOC and HCHO were conducted in five rooms of SDE, NUS. The collection data was trained and tested by 4 machine learning algorithms including support vector machine (SVM), Gaussian processes (GP), M5P and backpropagation neural network (BPNN). Overall, SVM scored the highest in performance evaluation because it has the highest average prediction accuracy and fewer overfitting in the test data. High predictability due to large autocorrelation was observed in the pattern analysis of CO2 and TVOC. Accurate results were achieved by SVM for CO2 and TVOC, with mean MAPE of being 1.87% and 2.30%, respectively. In contrast, low autocorrelation indicated the hidden mode of HCHO data was more difficult to capture than CO2 and TVOC. The small R-2 between predicted and actual values of HCHO demonstrated low predictability, ranging from 0.0008 to 0.0215.
机译：本文提出了有关使用机器学习方法预测TVOC和HCHO的新发现。在国大SDE的五个房间中连续测量CO2，TVOC和HCHO。收集数据通过4种机器学习算法进行了训练和测试，包括支持向量机（SVM），高斯过程（GP），M5P和反向传播神经网络（BPNN）。总体而言，SVM在性能评估中得分最高，因为它具有最高的平均预测准确性，并且在测试数据中的过拟合更少。在CO2和TVOC的模式分析中，由于较大的自相关性，具有很高的可预测性。通过SVM获得的CO2和TVOC的准确结果，平均MAPE分别为1.87％和2.30％。相反，低自相关性表明HCHO数据的隐藏模式比CO2和TVOC更难捕获。 HCHO的预测值与实际值之间的小R-2表明可预测性较低，范围为0.0008至0.0215。

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