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• This paper is focused on predicting price signals for energy arbitrage of storage systems. • A methodology is presented based on feature selection tools and classification techniques. • A Li-Ion battery in the Ontario competitive electricity market is considered as a case study. • Results indicate that the proposed methodology outperforms alternative point forecasting models. When participating in a competitive electricity market, energy storage systems could deliver various services and stack multiple revenue streams. One potential venue for gaining profit is energy arbitrage, i.e., to harvest the price differential that might exist in some markets between the peak and off-peak hours. To take advantage of price arbitrage, however, it is necessary to have an insight into the price fluctuations of upcoming hours. In this paper, we propose a method for generating predictive electricity price signals to help storage operators make arbitrage decisions. The proposed method delivers signals that are integrated into an optimization platform to schedule the arbitrage operation of a storage system. We examine a Lithium-ion battery as the choice of storage system and the Ontario's competitive electricity market in Canada is considered as the case study. We compare the performance of the proposed approach against the cases that alternative point forecasts of the electricity price are used for self-scheduling of the storage system. The results show that the obtained arbitrage profit of the storage system can be increased significantly using the proposed approach. [ABSTRACT FROM AUTHOR]