An evolving type-2 neural fuzzy inference system with fuzzy rule interpolation (eT2FIS++) with its application in straddle option trading - Zeng, Ye (2016)
Fuzzy neural networks are often used for modelling dynamic data streams and the systems keep evolving from offline to online, innovating and adding new schemes to address each individual issue of sparsity, non-linearity and time-variants in the datasets. The research has been widely applied to different areas such as traffic control, flood or rain prediction and financial worlds. In particular, it is topical to model the data in financial markets. However, many existing systems are incapable of handling sparse and dynamic time series data streams such as option trading data in the financial markets. Interpolation and extrapolation are one of the most popular techniques in handling the sparsity in the datasets.