  In recent years, marked temporal point processes (MTPPs) have emerged as a
powerful modeling machinery to characterize asynchronous events in a wide
variety of applications. MTPPs have demonstrated significant potential in
predicting event-timings, especially for events arriving in near future.
However, due to current design choices, MTPPs often show poor predictive
performance at forecasting event arrivals in distant future. To ameliorate this
limitation, in this paper, we design DualTPP which is specifically well-suited
to long horizon event forecasting. DualTPP has two components. The first
component is an intensity free MTPP model, which captures microscopic or
granular level signals of the event dynamics by modeling the time of future
events. The second component takes a different dual perspective of modeling
aggregated counts of events in a given time-window, thus encapsulating
macroscopic event dynamics. Then we develop a novel inference framework jointly
over the two models % for efficiently forecasting long horizon events by
solving a sequence of constrained quadratic optimization problems. Experiments
with a diverse set of real datasets show that DualTPP outperforms existing MTPP
methods on long horizon forecasting by substantial margins, achieving almost an
order of magnitude reduction in Wasserstein distance between actual events and
forecasts.
