The invention provides a space-time ARIMA traffic prediction method based on a neural network. The method comprises the following steps: decomposing original traffic data into a plurality of subsets through a time perception decomposition strategy; splitting each subset into period and time period data to form a training data set and a test data set; a space-time ARIMA network model is constructed; training a space-time ARIMA network model by using the training data set; and predicting a test data set by using the trained space-time ARIMA network model. According to the method, the time perception decomposition strategy and the space-time ARIMA are combined, the feature information of time and space is extracted at the same time, and the space-time dependence information in different time periods can be better captured, so that the traffic prediction effect is improved; according to the method, the time attribute of traffic data is considered, so that the problem of time-varying spatial dependency is solved; according to inherent distribution and a transition period of the data; compared with the existing traffic prediction method, the method provided by the invention can predict an accurate traffic prediction result.
本发明提供一种基于神经网络的时空ARIMA交通预测方法,包括通过时间感知分解策略将原始交通数据分解成多个子集;将每个子集拆分成周期与时间段数据,组成训练数据集和测试数据集;构建时空ARIMA网络模型;并利用训练数据集训练时空ARIMA网络模型;利用训练好的时空ARIMA网络模型对测试数据集进行预测。本发明结合了时间感知分解策略和时空ARIMA,同时提取了时间与空间的特征信息,并且可以更好的捕捉到不同时间段下的时空依赖信息,从而提升了交通预测的效果;本发明考虑交通数据的时间属性,从而处理时变空间依赖性问题;根据数据的固有分布和过渡周期;与现有交通预测方法相比,本发明能够预测出精确的交通预测结果。
Space-time ARIMA traffic prediction method based on neural network
一种基于神经网络的时空ARIMA交通预测方法
2025-02-21
Patent
Electronic Resource
Chinese
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