The price forecasting is one of the most important issues in electricity markets. For this purpose, an accurate prediction model is demanded for optimal operation as well as planning in power system. In this work, a novel approach composed of Wavelet Transform and Takagi-Sugeno (TS) fuzzy rule-based system is proposed for day-ahead price forecasting of electricity markets. In this method, the input of price data is clustered by TS fuzzy model. In the identification of the TS fuzzy model, a hyperplane prototype fuzzy clustering model is proposed, which obtain the rules. Furthermore, in this model, a new stochastic search algorithm is applied to optimize the clustering objective function. To implement the proposed forecast strategy, the price data is first decomposed by Wavelet Transform (WT). Then, each produced wavelet component is filtered by two stage feature selection based on mutual information. Afterward, the hybrid fuzzy neural network-based forecast engine is used to predict the future values of each price component. This model, is tested on real-world electricity markets of Ontario and New England through the comparison with other techniques. Obtained results demonstrate the validity of proposed technique. (C) 2017 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.