  Lung tumor tracking for radiotherapy requires real-time, multiple-step ahead
forecasting of a quasi-periodic time series recording instantaneous tumor
locations. We introduce a location-mixture autoregressive (LMAR) process that
admits multimodal conditional distributions, fast approximate inference using
the EM algorithm and accurate multiple-step ahead predictive distributions.
LMAR outperforms several commonly used methods in terms of out-of-sample
prediction accuracy using clinical data from lung tumor patients. With its
superior predictive performance and real-time computation, the LMAR model could
be effectively implemented for use in current tumor tracking systems.
