  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.
