TY - JFULL AU - Shigeaki Sakurai and Makino Kyoko and Shigeru Matsumoto PY - 2014/3/ TI - A Prediction of Attractive Evaluation Objects Based On Complex Sequential Data T2 - International Journal of Computer and Information Engineering SP - 357 EP - 366 VL - 8 SN - 1307-6892 UR - https://publications.waset.org/pdf/9998004 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 86, 2014 N2 - This paper proposes a method that predicts attractive evaluation objects. In the learning phase, the method inductively acquires trend rules from complex sequential data. The data is composed of two types of data. One is numerical sequential data. Each evaluation object has respective numerical sequential data. The other is text sequential data. Each evaluation object is described in texts. The trend rules represent changes of numerical values related to evaluation objects. In the prediction phase, the method applies new text sequential data to the trend rules and evaluates which evaluation objects are attractive. This paper verifies the effect of the proposed method by using stock price sequences and news headline sequences. In these sequences, each stock brand corresponds to an evaluation object. This paper discusses validity of predicted attractive evaluation objects, the process time of each phase, and the possibility of application tasks. ER -