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