Java Deep Learning Cookbook

Loading training data

Data transformation is, as usual, the second phase after data extraction. The time series data we're discussing doesn't have any non-numeric fields or noise (it had already been cleaned). So we can focus on constructing the iterators from the data and loading them directly into the neural network. In this recipe, we will load univariate time series data for neural network training. We have extracted the synthetic control data and stored it in a suitable format so the neural network can process it effortlessly. Every sequence is captured over 60 time steps. In this recipe, we will load the time series data into an appropriate dataset iterator, which can be fed to the neural network for further processing.