Adaptive Tuning of Neural Networks Parameters for Time Series Prediction

Predictive Layer SA is developing a large number of systems related to trading in energy, commodity and stocks, which are based on forecasting time series data. Fluctuations and trends of financial target signals result from strong dynamic interactions with the external environment whose underlying factors are constantly changing in their relative contribution. As trading strategies require to adapt to changing market conditions and scale with increasing allocation of computing resources under strict time execution constraints, new research and algorithms need to be developed to model dynamically the final structure of the neural network (NN) – based predictive engines. Any gain in accuracy and robustness will be translated into a competitive advantage for the company and its customers and ultimately will determine the entire life-cycle of these products. Nowadays, Predictive Layer delivers solutions mainly based on the optimization of Ensemble Trees and static neural network architectures upon processing prior information from available time series data-sets.

The core algorithm needs to work on large set of financial data under strict time constraints. The novel NN architecture will select information and shape dynamically compared to traditional methods in order to achieve high performances and stability over time and ensure sufficient model diversity in the final decision process. The efficient design of an adaptive neural network architecture is extremely challenging from both scientific and engineering point of views due to the inherent complexity and ill-conditioning of the problem, the combination of multiple models and the execution time constraints.

Collaboration: PredictiveLayer SA

Funding: InnoSuisse