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

Abstract

In this paper, the concept of long memory systems for forecasting is developed. The pattern modelling and recognition system and fuzzy single nearest neighbour methods are introduced as local approximation tools for forecasting. Such systems are used for matching the 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 returns indices, as well as the stock returns of five FTSE 100 companies and compare the results of the two different systems with those of exponential smoothing and random walk on seven different error measures. The results show that pattern recognition based approaches in time-series forecasting are highly accurate. Simple theoretical trading strategies are also mentioned, highlighting real applications of the system.