Abstract
Cerebellar model articulation controller neural network (CMAC NN) has many advantages, such as very fast learning, reasonable generalization capability and robust noise resistance. Thus, CMAC NNs are conventionally used in robot control. To solve financial time series forecasting, this paper presents an efficient CMAC NN scheme. The proposed CMAC NN transforms continuous values of input variables to discrete indexes by using a quantization operator. To enhance generalization ability, the CMAC NN employs high quantization resolution and a large generalization size. To perform many-to-few mappings, the CMAC NN uses an efficient and fast hashing code based on bitwise XOR operator. The proposed CMAC NN was used to Nikkei 225 closing cash indexes collected from Japanese stock market. The forecasting results of the proposed CMAC NN were compared with those of support vector regression (SVR), which is statistical/ machine learning algorithm. Experimental results indicate that the performance of the CMAC NN was better than SVR in the tested case. Therefore, the CMAC NN may be considered as an efficient tool for forecasting financial time series.
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Lu, CJ., Wu, JY. (2010). Forecasting Financial Time Series via an Efficient CMAC Neural Network. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_9
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DOI: https://doi.org/10.1007/978-3-642-12990-2_9
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