论文标题
多维时间序列预测的自适应图卷积网络框架
Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction
论文作者
论文摘要
在现实世界中，在许多情况下需要长期的序列时间序列预测（LSTF），例如功耗预测和空气质量预测。Sulti维度二维的长期序列模型对该模型具有更严格的要求，该模型不仅需要有效地捕获输入和输出之间的准确长期依赖性，而且还需要捕捉到差异范围之间的良好型号。预测。但是，该模型在多维预测中仍然存在一些缺陷，它无法很好地捕获不同维度之间的关系。我们改善了信息，以解决其在多维预测中的缺点。首先，我们引入了一个自适应图神经网络，以捕获大多数时间序列预测中隐藏的维度依赖性。其次，我们将自适应图卷积网络集成到各种时空系列预测模型中，以解决它们无法捕获不同维度之间关系的缺陷。第三，在对多个数据集进行实验测试之后，我们的框架的准确性在被引入模型后提高了约10 \％。
In the real world, long sequence time-series forecasting (LSTF) is needed in many cases, such as power consumption prediction and air quality prediction.Multi-dimensional long time series model has more strict requirements on the model, which not only needs to effectively capture the accurate long-term dependence between input and output, but also needs to capture the relationship between data of different dimensions.Recent research shows that the Informer model based on Transformer has achieved excellent performance in long time series prediction.However, this model still has some deficiencies in multidimensional prediction,it cannot capture the relationship between different dimensions well. We improved Informer to address its shortcomings in multidimensional forecasting. First,we introduce an adaptive graph neural network to capture hidden dimension dependencies in mostly time series prediction. Secondly,we integrate adaptive graph convolutional networks into various spatio-temporal series prediction models to solve the defect that they cannot capture the relationship between different dimensions. Thirdly,After experimental testing with multiple data sets, the accuracy of our framework improved by about 10\% after being introduced into the model.
