Abstract: Time-series is ubiquitous across applications, such as transportation,
finance and healthcare. Time-series is often influenced by external factors,
especially in the form of asynchronous events, making forecasting difficult.
However, existing models are mainly designated for either synchronous
time-series or asynchronous event sequence, and can hardly provide a synthetic
way to capture the relation between them. We propose Variational Synergetic
Multi-Horizon Network (VSMHN), a novel deep conditional generative model. To
learn complex correlations across heterogeneous sequences, a tailored encoder
is devised to combine the advances in deep point processes models and
variational recurrent neural networks. In addition, an aligned time coding and
an auxiliary transition scheme are carefully devised for batched training on
unaligned sequences. Our model can be trained effectively using stochastic
variational inference and generates probabilistic predictions with Monte-Carlo
simulation. Furthermore, our model produces accurate, sharp and more realistic
probabilistic forecasts. We also show that modeling asynchronous event
sequences is crucial for multi-horizon time-series forecasting.