This work aims to propose deep learning technique that combines convolutional neural network with single multiplicative neuron model to optimize delay value and improving forecasting efficiency in predicting cryptocurrency prices. This model is proposed with the intent of tackling high nonlinearity present in the cryptocurrency prices. A univariate time series of daily price of two cryptocurrencies Bitcoin and Ethereum is considered to validate the proposed model. Multiple experiments have been performed to validate the proposed deep learning model, and RMSE value is used as the error criteria. The least RMSE value is used in evaluating optimal delay value. The proposed model is 23–33%; it is more accurate in forecasting compared to the single multiplicative neuron model. The results obtained can give valuable insights for decision-making. The proposed approach paves way for future research on time series prediction and support easy adaptation across various time series and with different scenarios.