  Recently, the incorporation of both temporal features and the correlation
across time series has become an effective approach in time series prediction.
Spatio-Temporal Graph Neural Networks (STGNNs) demonstrate good performance on
many Temporal-correlation Forecasting Problem. However, when applied to tasks
lacking periodicity, such as stock data prediction, the effectiveness and
robustness of STGNNs are found to be unsatisfactory. And STGNNs are limited by
memory savings so that cannot handle problems with a large number of nodes. In
this paper, we propose a novel approach called the Temporal-Correlation Graph
Pre-trained Network (TCGPN) to address these limitations. TCGPN utilize
Temporal-correlation fusion encoder to get a mixed representation and
pre-training method with carefully designed temporal and correlation
pre-training tasks. Entire structure is independent of the number and order of
nodes, so better results can be obtained through various data enhancements. And
memory consumption during training can be significantly reduced through
multiple sampling. Experiments are conducted on real stock market data sets
CSI300 and CSI500 that exhibit minimal periodicity. We fine-tune a simple MLP
in downstream tasks and achieve state-of-the-art results, validating the
capability to capture more robust temporal correlation patterns.
