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With the deregulation of power market and the increasing penetration of renewable energy, the core role of demand side management (DSM) has become even more prominent. In this sense, there is an urgent need for all market participants to identify the pivotal aspects of electricity market price fluctuation effectively and anticipate its future trend. For certain applications such as DSM, considering the high volatility and nonlinear of real-time electricity price, we can approximate the interval prediction to achieve the multi-classification that relies on critical pattern recognition of entire category of the price sequence. Therefore, this paper presents a study on the utilization of a novel electricity price classification framework which consists of Bayesian extreme learning machine (BELM) model, minimum redundancy maximum relevance (MRMR) algorithm, and multivariate sequence segmentation (MSS). Considering many …