The invention provides a traffic flow prediction method, system and model based on a block data fusion ARIMA model. The method comprises the following steps: carrying out data reconstruction and feature extraction on preprocessed data; combining a feature extraction result with the original data to form a plurality of data blocks; training the block data fusion ARIMA model on the basis of the data blocks; and carrying out residual analysis on the trained block data fusion ARIMA model, applying a residual to a random forest algorithm for modeling, and combining a residual prediction value obtained by the random forest algorithm with a prediction value of the ARIMA model to predict the traffic flow. According to the method, random forest algorithm modeling is carried out on the residual error of the ARIMA model, the defects of the ARIMA model are compensated by utilizing the strong capability of the random forest, and a complex mode and a nonlinear relationship in data can be better processed, so that the accuracy and reliability of prediction are improved.
本发明提出一种基于块数据融合ARIMA模型的交通流预测方法,系统和模型,所述方法通过对预处理后的数据进行数据重构和特征提取;并将特征提取结果与所述原始数据结合,形成多个数据块;基于所述数据块对所述块数据融合ARIMA模型进行训练;对训练好的块数据融合ARIMA模型进行残差分析,将残差应用到随机森林算法中建模,将随机森林算法得到的残差预测值与ARIMA模型的预测值进行组合预测交通流。本发明对ARIMA模型的残差进行随机森林算法建模,利用随机森林的强大能力来补偿ARIMA模型的不足,能够更好地处理数据中的复杂模式和非线性关系,从而提高预测的准确性和可靠性。
Traffic flow prediction method, system and model based on block data fusion ARIMA model
基于块数据融合ARIMA模型的交通流预测方法,系统和模型
2024-12-27
Patent
Electronic Resource
Chinese
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