Before revision:  A further simple modification enables them to forecast not the mean, but arbitrary quantiles of the distribution . And one last thing to make this all work: the CNN forecaster's complexity and flexibility requires much data, that is, preferably multiple time series. When this is met, the proposed QCNN framework can be competitive. It is demonstrated on a financial problem of huge practical importance: Value at Risk forecasting. By contributing to the stability of financial systems, deep learning may find one further way to improve our lives .
After revision:  A further simple modification enables them to forecast not the mean, but arbitrary quantiles of the distribution , and thus allows them to be applied to VaR-forecasting. The proposed model can learn from the price history of different assets, and it seems to produce fairly accurate forecasts .