Fuzzy associative learning of feature dependency for time series forecasting

Neuro-fuzzy system (NFS) has successfully been widely applied in solving problems across diverse fields, such as signal detection, fault detection, and forecasting. In recent years, many forecasting problems require the processing and learning of large number of dynamic data streams. Existing systems are inadequate in handling this type of complex problem. This paper presents a learning system that incorporates an evolving correlation-based feature selector to handle the high dimensionality of the data streams, and an evolving NFS to sequentially model and extract fuzzy knowledge about these data streams. The proposed system requires no prior knowledge of the data, reads the stream of data in a single pass, and accounts for the time-varying characteristics of the data. These three features allow the system to handle large and dynamic data. The effectiveness of the proposed system is validated on both synthetic and real-world problems. The experiments illustrate the viability of the proposed learning technique, and exemplifies how it can outperform existing NFS. Experiment on real-world stock price forecasting shows a remarkable reduction of error rate by 15.4%.