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Piggio, T. and Girosi, F. (1989) A Theory of Networks for Approximation and Learning. A.I. Memo No. 1140, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge.

has been cited by the following article:

• TITLE: Predictive Analytics on CSI 300 Index Based on ARIMA and RBF-ANN Combined Model

  AUTHORS: Lyuxun Yang, Xi Cheng

  KEYWORDS: ARIMA, RBF-ANN, CSI 300 Index, Prediction Model

  JOURNAL NAME: Journal of Mathematical Finance, Vol.5 No.4, November 25, 2015

  ABSTRACT: The time series of share prices is a highly noised, non-stationary chaotic system which possesses both linear and non-linear characteristics. The alternative of either linear or non-linear prediction models is of its inherent limitation. The paper establishes an ARIMA and RBF-ANN combined model and makes a short-term prediction on the time series of CSI 300 index by choosing various typical input variables. Results show that the combined model with multiple input indicators, compared with single ARIMA model, single RBF-ANN model, or models with single input variable, is of higher precision.