针对PM2.5单时间序列数据的动态调整预测模型

针对细颗粒物PM2.5的浓度预测，本文提出了基于单时间序列数据的动态调整模型.在动态指数平滑算法中，指数平滑次数与参数基于样本数据并借助二分查找进行调整.在动态马尔科夫模型中，马尔科夫链的残差状态数、隐马尔科夫模型的隐状态数、连续样本数和阈值参数都通过训练数据加以调整.动态调整模型将指数平滑法和马尔科夫模型有效结合起来，指数平滑法得到的预测值由马尔科夫模型进行校正，从而提高预测准确度.基于大量实际PM2.5数据进行测试，验证了算法的有效性.并与其他现有的灰色模型、人工神经网络、自回归滑动平均模型、支持向量机等方法进行了对比，表明所提模型能够得到精度更高的预测结果.本文模型不局限于PM2.5数据，还可应用于其他类型的数据预测.

Prediction Model With Dynamic Adjustment for Single Time Series of PM2.5

A prediction model is proposed with dynamic adjustment for single time series of PM2.5 data. In the dynamic exponential smoothing algorithm, the optimal exponent and parameter are determined by sample data and binary search. In the dynamic Markov model, the state number of residual errors from Markov chain, numbers of hidden and observable states, and threshold parameters from hidden Markov model, are all decided dynamically based on training data. The proposed dynamic model combines the two models effectively, and predictions from exponential smoothing are adjusted by Markov model to increase the accuracy. Using a large number of real PM2.5 data, efficiency of the proposed model has been tested. Compared with the existing popular methods, such as gray model, artificial neural networks, auto-regressive moving average, support vector machine, the proposed model can obtain prediction results with the best precision. In addition to PM2.5, the dynamically adjusted prediction model may be used for prediction of other type single time series of data.