Time series forecasting is important across various domains for decision-making. In particular, financial time series such as stock prices can be hard to predict as it is difficult to model short-term and long-term temporal dependencies between data points. Convolutional Neural Networks (CNN) are good at capturing local patterns for modeling short-term dependencies. However, CNNs cannot learn long-term dependencies due to the limited receptive field. Transformers on the other hand are capable of learning global context and long-term dependencies. In this paper, we propose to harness the power of CNNs and Transformers to model both short-term and long-term dependencies within a time series, and forecast if the price would go up, down or remain the same (flat) in the future. In our experiments, we demonstrated the success of the proposed method in comparison to commonly adopted statistical and deep learning methods on forecasting intraday stock price change of S&P 500 constituents.

翻译：时间序列预测对于各领域决策制定具有重要意义。特别地，股票价格等金融时间序列因难以建立数据点间短期与长期时间依赖关系而极具预测挑战。卷积神经网络能通过捕捉局部模式有效建模短期依赖，但其感受野有限导致无法学习长期依赖关系。而Transformer则具备学习全局语境与长期依赖的能力。本文提出利用CNN与Transformer的协同优势，同时建模时间序列中的短期与长期依赖关系，预测未来价格走势（上涨、下跌或持平）。实验结果表明，相较于常用统计方法与深度学习方法，本方法在标普500成分股日内股价变动预测任务中取得了显著成效。