  Forecasting based on financial time-series is a challenging task since most
real-world data exhibits nonstationary property and nonlinear dependencies. In
addition, different data modalities often embed different nonlinear
relationships which are difficult to capture by human-designed models. To
tackle the supervised learning task in financial time-series prediction, we
propose the application of a recently formulated algorithm that adaptively
learns a mapping function, realized by a heterogeneous neural architecture
composing of Generalized Operational Perceptron, given a set of labeled data.
With a modified objective function, the proposed algorithm can accommodate the
frequently observed imbalanced data distribution problem. Experiments on a
large-scale Limit Order Book dataset demonstrate that the proposed algorithm
outperforms related algorithms, including tensor-based methods which have
access to a broader set of input information.
