Data with time attributions such as price, load, and stock, which directly reflect the variation tendency, are the most common type of data character available. However, it is difficult to predict complex and volatile time-series character data. Further, most density cluster methods employ existing data to train the initial radius; however, a certain density radius is hard to be made suitable for continuously generated on-going datasets. Therefore, how to select a suitable timespan according to the time-series character in a way that makes it possible to support an adaptive updated density radius for real-time calculation is a core process. In this paper, a self-adaptive multi-density (SAMD) prediction model is proposed for solving the dynamic density radius selection problem in time-series data so as to improve the accuracy of real-time prediction. This multi-density clustering method can effectively shorten the iteration times and achieve dynamic clustering by the proposed jump sequence, which can optimize the jump points in the electricity price sequence. Moreover, we especially focus on the time interval features and other multi-source influencing factors together to construct the multi-core function with double-layer optimization to calculate the weighted coefficients, which have good adaptability and improve the classification and recognition performance. The experimental results show that the model had higher prediction accuracy and reduced processing time consumption in order to achieve real-time prediction.