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Poster
Neural Jump Stochastic Differential Equations
Junteng Jia · Austin Benson

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #111
in Applications -- Time Series Analysis »

Many time series are effectively generated by a combination of deterministic continuous flows along with discrete jumps sparked by stochastic events. However, we usually do not have the equation of motion describing the flows, or how they are affected by jumps. To this end, we introduce Neural Jump Stochastic Differential Equations that provide a data-driven approach to learn continuous and discrete dynamic behavior, i.e., hybrid systems that both flow and jump. Our approach extends the framework of Neural Ordinary Differential Equations with a stochastic process term that models discrete events. We then model temporal point processes with a piecewise-continuous latent trajectory, where the discontinuities are caused by stochastic events whose conditional intensity depends on the latent state. We demonstrate the predictive capabilities of our model on a range of synthetic and real-world marked point process datasets, including classical point processes (such as Hawkes processes), awards on Stack Overflow, medical records, and earthquake monitoring.

Keywords: Time Series Analysis · Applications
[ Paper]  [ Slides] 

Author Information

Junteng Jia (Cornell)
Austin Benson (Cornell University)

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