It is a challenging problem to predict trends of futures prices with traditional econometric models as one needs to consider not only futures' historical data but also correlations among different futures. Spatial-temporal graph neural networks (STGNNs) have great advantages in dealing with such kind of spatial-temporal data. However, we cannot directly apply STGNNs to high-frequency future data because future investors have to consider both the long-term and short-term characteristics when doing decision-making. To capture both the long-term and short-term features, we exploit more label information by designing four heterogeneous tasks: price regression, price moving average regression, price gap regression (within a short interval), and change-point detection, which involve both long-term and short-term scenes. To make full use of these labels, we train our model in a continual manner. Traditional continual GNNs define the gradient of prices as the parameter important to overcome catastrophic forgetting (CF). Unfortunately, the losses of the four heterogeneous tasks lie in different spaces. Hence it is improper to calculate the parameter importance with their losses. We propose to calculate parameter importance with mutual information between original observations and the extracted features. The empirical results based on 49 commodity futures demonstrate that our model has higher prediction performance on capturing long-term or short-term dynamic change.
翻译:传统经济计量模型难以预测期货价格趋势,因为不仅需要考虑期货的历史数据,还需考虑不同期货之间的相关性。时空图神经网络(STGNNs)在处理此类时空数据方面具有巨大优势。然而,我们无法直接将STGNNs应用于高频期货数据,因为期货投资者在做决策时必须考虑长期和短期特征。为捕捉长期和短期特征,我们通过设计四种异构任务(价格回归、价格移动平均回归、价格区间回归和变点检测),来利用更多标签信息,涉及到长期和短期方面的场景。为了充分利用这些标签,我们以连续的方式对模型进行训练。传统的连续GNN将价格的梯度定义为克服灾难性遗忘(CF)的重要参数。不幸的是,四个异构任务的损失处于不同的空间之中,无法用它们的损失来计算参数重要性。我们提出了用原始观察和提取特征之间的互信息来计算参数重要性的方法。基于49个商品期货的实验结果表明,我们的模型在捕捉长期或短期动态变化方面具有更高的预测性能。