论文标题
使用时间序列数据的图形神经网络用于模型推荐
Graph Neural Networks for Model Recommendation using Time Series Data
论文作者
论文摘要
时间序列预测旨在预测未来的价值，以帮助利益相关者做出适当的战略决策。这个问题在所有行业和领域都相关，从财务数据到需求到预测。但是，从业人员选择用于预测任务的合适模型仍然具有挑战性。考虑到这一点，我们提出了基于图神经网络的模型体系结构，以提供时间序列预测的模型建议。我们在三个相关数据集上验证了我们的方法，并将其与16多种技术进行了比较。我们的研究表明，所提出的方法的性能优于目标基准和最新技术，包括元学习。结果表明，GNN作为时间序列预测中模型建议的方法的相关性和适用性。
Time series prediction aims to predict future values to help stakeholders make proper strategic decisions. This problem is relevant in all industries and areas, ranging from financial data to demand to forecast. However, it remains challenging for practitioners to select the appropriate model to use for forecasting tasks. With this in mind, we present a model architecture based on Graph Neural Networks to provide model recommendations for time series forecasting. We validate our approach on three relevant datasets and compare it against more than sixteen techniques. Our study shows that the proposed method performs better than target baselines and state of the art, including meta-learning. The results show the relevancy and suitability of GNN as methods for model recommendations in time series forecasting.
