High-order fuzzy time series self-adaption prediction method based on spectral clustering - Chun-nan ZHOU; Shao-bin HUANG; Rong-hua CHI; Ya LI; Da-peng LANG (Tongxin xuebao)
A fuzzy time series self-adaption prediction method based on spectral clusterin and data characteristics was proposed. First, based on spectral clustering and the racteristics of data, the number and scope of the discourses was obtained to convert into fuzzy time series self-adaptively. Then, fuzzy relationships based on Markov probability model was presented, and the multi-steps, high-order and steady fuzzy relationship are gotten.Finally, proposed meted obtained the probable fuzzy states, and got its predicted values based on defuzzification methods. Experiments on real-world and synthetic time series data indicate the rationality and effectiveness of the proposed method.