| Summary: | The rough set pseudo outer-product fuzzy neural network (RSPOP FNN) is a
member of the POPFNN family known for high accuracy and interpretability, and
also uses rough set theory to perform attribute and rule reduction. An incrementalensemble
RSPOP FNN, named ieRSPOP, is proposed and implemented in this
project. This new system aims to further improve the capability of RSPOP by using
an incremental learning algorithm. Issues with incremental rough set attribute
reduction are also addressed using ensemble learning. ieRSPOP utilizes the
compositional rule of inference (CRI) method due to its dominance in the field of
approximate reasoning.
The performance of ieRSPOP is evaluated through several time series
benchmark experiments and stock data and analyzed against existing incremental and
non-incremental architectures, and results are promising.
The project also attempts to forecast real life stock price volatility. The
concepts of econometric models of generalized auto-regressive conditional
heteroskedasticity (GARCH) and intraday volatility indicators are used. When
combined with GARCH concepts and indicators, the performance of ieRSPOP
approaches the accuracy of well-known GARCH models. ieRSPOP also provides the
added benefit of generating IF-THEN fuzzy rules which describe the GARCH
volatility model.
|
|---|