We develop the spline-DCS model and apply it to trade volume prediction, which remains a highly non-trivial task in high-frequency finance. Our application illustrates that the spline-DCS is computationally practical and captures salient empirical features of the data such as the heavy-tailed distribution and intra-day periodicity very well. We produce density forecasts of volume and compare the model's predictive performance with that of the state-of-the-art volume forecasting model, named the component-MEM, of Brownlees et al. (2011). The spline-DCS significantly outperforms the component-MEM in predicting intra-day volume proportions.