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

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... ork, 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. Keywords: TS model MISO-NF network, fuzzy C-Means clustering algorithm, accelerated Levenberg-Marquardt algorithm, financial stock market. Neuro-fuzzy Systems Selection for modeling and forecasting A neuro-fuzzy network with an improved training algorithm for MIMO case was developed by Palit and Popovic [3, 4] and used for forecasting of electrical load data. However, the same network for MISO case was developed by Palit and Babuška [2] . Compared to ANFIS by Jang [1], the neuro-fuzzy models of [2, 3, 4] have achieved better model accuracy and faster training performance. In addition, the neuro-fuzzy model of [3, 4, 5] is an upgraded version of Takagi-Sugeno type multiple input single output NF network. Feedforward type MIMO-NF network is proposed by Palit and Popovic [4, 3] , as shown in Fig. 1 . For our stock market forecasting application we will also use the same model but with number of outputs equals to one. Neuro-fuzzy model as shown in Fig.