Application of Modified Complex Neural Fuzzy System in Time Series Forecasting

• Main Authors: Ching-Yu Wu, 吳清鈺
• Other Authors: 李俊賢
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/855f43

碩士 === 國立中央大學 === 資訊管理學系 === 106 === This study proposes a modified complex fuzzy neural system for the problem of time series prediction. The modified way is that this study proposes an error feedback method according to the errors between the model outputs and the targets. This paper feeds errors back to the model's consequent part to increase the model prediction accuracy. In the selection of model input, this study proposes a multi-targets feature selection method based on the theory of Shannon entropy. The main purpose is to select features by calculating the amount of information provided by the features for the target, and using a set of feature selection strategies to do the multi-targets feature selection. In the establishment of the model, this paper changes the traditional subjective way to objective way by using the training data itself to construct the model setting. Complex fuzzy sets (CFSs) have better interpretive capabilities than traditional fuzzy sets. They can deliver a larger amount of information than traditional fuzzy sets in neural fuzzy systems, increase the effectiveness of model prediction, and let the model predict multi-targets at the same time. In the model parameter learning phase, Particle swarm optimization (PSO) and Recursive least squares estimator (RLSE) are combined to form a PSO-RLSE hybrid algorithm. PSO is used to optimize the parameters of the model's premise part, while RLSE is responsible for the parameters of the model's consequent part. This concept of divide-and-conquer can reduce the dimension of the model parameters, increase the probability of finding the best solution of the model parameters, and reduce the overall training time of the model. This study uses time series of the financial market to conduct the multi-targets forecasting experiments. The experimental results show that the model proposed in this paper has better forecasting abilities than other literatures.