a b s t r a c tThe study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling uncertainty and vagueness inherent in the data collected. A variety of forecasting models including high-order models have been devoted to improving forecasting accuracy. However, the high-order forecasting approach is accompanied by the crucial problem of determining an appropriate order number. Consequently, such a deficiency was recently solved by Li and Cheng [S.-T. Li, Y.-C. Cheng, Deterministic Fuzzy time series model for forecasting enrollments, Computers and Mathematics with Applications 53 (2007) 1904-1920] using a deterministic forecasting method. In this paper, we propose a novel forecasting model to enhance forecasting functionality and allow processing of two-factor forecasting problems. In addition, this model applies fuzzy cmeans (FCM) clustering to deal with interval partitioning, which takes the nature of data points into account and produces unequal-sized intervals. Furthermore, in order to cope with the randomness of initially assigned membership degrees of FCM clustering, Monte Carlo simulations are used to justify the reliability of the proposed model. The superior accuracy of the proposed model is demonstrated by experiments comparing it to other existing models using real-world empirical data.

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In recent years, R&D institutes have encountered various intensified challenges. New instruments are needed to manage knowledge-related activities more effectively and efficiently. This paper presents and discusses the lessons learned from a case study in fostering knowledge management (KM) initiatives and systems in a research-oriented institute serving the metal industry, specifically the Metal Industries Research and Development Centre (MIRDC) in Taiwan. We perform a comparative review of the experience of embarking on KM among Taiwanese R&D institutes, a very rarely performed job. Following this, we investigate, by conducting the primary and secondary researches, how MIRDC has adopted a five-stage approach to develop a deliberate framework of KM deployment in order to manipulate the KM operations in the context of a Chinese R&D institute. The MIRDC case demonstrates a sophisticated KM process that provides an activity-based perspective of the plan, control, coordination and evaluation framework in an R&D workspace. This paper argues that well-defined deployment frameworks embody qualities of goal pursuing that are important to KM activities and compel managers to examine more closely how to realize the KM initiatives. This paper also reveals that a rigid hierarchical R&D structure inhibits the dynamics of the knowledge cycle due to technology segmentation. A parallel R&D structure supported by mission offices and a 'pioneer and innovation program' that is cross-departmental and industry-focused can positively motivate horizontal 'coopertition' networking so as to better exploit and leverage knowledge assets. The practices applied in these elemental KM activities are useful to other R&D organizations by suggesting how each of the KM activities can be configured and implemented.

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