This study develops an improved fuzzy time series method via adjustment of the latest value factor and previous error patterns. There are many fuzzy extended applications in the literature, and the fuzzy time series is one successful implementation of fuzzy logical modelling. Fuzzy time series have been studied for over a decade, and many researchers have proposed to remove some of the drawbacks of the initial fuzzy time series algorithm. In this paper, fuzzy integrated logical forecasting (FILF) and extended FILF (E-FILF) algorithms are suggested for short term forecasting purposes. Empirical studies are performed over the Baltic Dry Index (BDI), and indicate the superiority of the proposed approach compared to conventional benchmark methods. (C) 2010 Elsevier Ltd. All rights reserved.