CNNs for time-series data – predicting returns
CNNs were originally developed to process image data and have achieved superhuman performance on various computer vision tasks. As discussed in the first section, time-series data has a grid-like structure similar to that of images, and CNNs have been successfully applied to one-, two- and three-dimensional representations of temporal data.
The application of CNNs to time series will most likely bear fruit if the data meets the model's key assumption that local patterns or relationships help predict the outcome. In the time-series context, local patterns could be autocorrelation or similar non-linear relationships at relevant intervals. Along the second and third dimensions, local patterns imply systematic relationships among different components of a multivariate series or among these series for different tickers. Since locality matters, it is important that the data is organized accordingly, in contrast to feed-forward...
