A fuzzy time series prediction method based on consecutive values

This paper presents a time series prediction method using a fuzzy rule-based system. In conventional methods, predicting x(n+k) requires past data such as x(n), x(n-l), ...x(n-m), where k and m are positive integers. However, a serious problem of those methods is that they cannot properly handle non-stationary data whose long-term mean is floating. To cope with this, a new learning method utilizing the difference of consecutive values in a time series is suggested. Computer simulations showed improved results for various time series.