Time series exist widely in either nature or society such that the research on analysis of time series has great significance. However, considering the nonlinearity and uncertainty, the prediction of time series is still an open problem. In this paper, by means of the intuitionistic fuzzy set theory, we proposed a novel time series prediction scheme based on intuitionistic fuzzy cognitive map. In the previous research, intuitionistic fuzzy cognitive map, as a kind of knowledge-based modeling tool, is mainly used in decision-making field, where concept structure and weight matrix are usually obtained from experience of experts. To tackle with the diversity of time series, the proposed algorithm constructs the conceptual structure of cognitive map and weight matrix directly from raw sequential data, which effectively enlarges the application range by reducing human participation. Moreover, in order to appropriately calculate the hesitation degree, which is the key role for the application of intuitionistic fuzzy sets, we propose a real-time adjustable hesitation degree calculation scheme. By using this proposed method, hesitation degree can be adaptively adjusted by combining Femi formula with dynamic membership degree. A number of experiments are implemented to reveal feasibility and effectiveness of the proposed schemes.

时间序列在自然界或社会中都广泛存在，因此对时间序列分析的研究具有重要的意义。但是，考虑到非线性和不确定性，时间序列的预测仍然是一个未解决的问题。本文通过直觉模糊集理论，提出了一种基于直觉模糊认知图的时间序列预测方案。在以前的研究中，直觉模糊认知图作为一种基于知识的建模工具，主要用于决策领域，通常从专家的经验中获得概念结构和权重矩阵。针对时间序列的多样性，该算法直接从原始序列数据中构造了认知图和权重矩阵的概念结构，通过减少人员参与有效地扩大了应用范围。此外，为了适当地计算犹豫度，这是直觉模糊集应用的关键作用，我们提出了一种实时可调的犹豫度计算方案。通过使用该方法，可以通过将Femi公式与动态隶属度相结合来自适应地调整犹豫度。进行了大量实验以揭示所提出方案的可行性和有效性。可以通过将Femi公式与动态隶属度相结合来自适应地调整犹豫度。进行了大量实验以揭示所提出方案的可行性和有效性。可以通过将Femi公式与动态隶属度相结合来自适应地调整犹豫度。进行了大量实验以揭示所提出方案的可行性和有效性。