Data-driven Neural Architecture Learning For Financial Time-series Forecasting

Understand

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.