Financial time series forecasts using fuzzy and long memory pattern recognition systems

In this paper, the concept of long memory systems for forecasting is developed. The Pattern Modelling and Recognition System (PMRS) and Fuzzy Single Nearest Neighbour (SNN) methods are introduced as local approximation tools for forecasting. Such systems are used for matching current state of the time-series with past states to make a forecast. In the past, the PMRS system has been successfully used for forecasting the Santa Fe competition data. In this paper, we forecast the FTSE 100 and 250 financial indices, as well as the stock prices of five FTSE 100 companies and compare the results of the two different systems, with that of Exponential Smoothing and Geometric Walk on seven different error measures. The results show that pattern recognition based approaches in time-series forecasting are highly accurate. A profit plot example is also given for the fuzzy system for various levels of transaction costs highlighting real applications of the system.