Long-term forecasting in financial stock market using accelerated LMA on neuro-fuzzy structure and additional fuzzy C-Means clustering for optimizing the GMFs.

The paper describes the combination of two modeling strategies between the accelerated Levenberg-Marquardt algorithm (accelerated LMA) on neuro-fuzzy approach and fuzzy clustering algorithm C-Means that can be used to forecast financial stock market such as Jakarta Stock Indices (JCI) using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network efficiently. The accelerated LMA algorithm is efficient in the common sense that it can bring the performance index of the network, such as the root mean squared error (RMSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA). The C-Means fuzzy clustering algorithm allows the selection of initial parameters of fuzzy membership functions, e.g. mean and variance parameters of Gaussian membership functions of neuro-fuzzy networks, which are otherwise selected randomly. The initial parameters of fuzzy membership functions, which result in low sum squared error (SSE) value with given training data of neuro-fuzzy network, are further fine tuned during the network training. As a final point, the above training algorithm is tested on TS type MISO neuro-fuzzy structure for long-term forecasting application of stock market in Indonesia.