  Predicting intraday trading volume plays an important role in trading alpha
research. Existing methods such as rolling means(RM) and a two-states based
Kalman Filtering method have been presented in this topic. We extend two states
into various states in Kalman Filter framework to improve the accuracy of
prediction. Specifically, for different stocks we utilize cross validation and
determine best states number by minimizing mean squared error of the trading
volume. We demonstrate the effectivity of our method through a series of
comparison experiments and numerical analysis.
