Learning Generative RNN-ODE for Collaborative Time-Series and Event Sequence Forecasting
IEEE Transactions on Knowledge and Data Engineering(2023)
Abstract
Time-series and event sequences are widely collected data types in real-world applications. Modeling and forecasting of such temporal data play an important role in an informed decision-making process. A major limitation of previous methods is that they either focus on time-series or events, rather than the combination of the two worlds. In fact, the two types of data often provide complementary information, emphasizing the necessity of jointly modeling the both. In this paper, we propose the RNN-ODE collaborative model for joint modeling and forecasting of heterogeneous time-series and event sequence data, which combines several useful techniques from both Bayesian and deep learning for its interpretability. Specifically, we devise a tailored encoder to combine the advances in deep temporal point processes models and variational recurrent neural networks. To predict the probability of event occurrence over an arbitrary continuous-time horizon, we base our model on the mathematical foundation of Neural Ordinary Differential Equations (NODE). Extensive experimental results on simulations and real data sets show that compared with existing methods, our integrated approach can achieve more competitive forecasting performance of both time-series and event sequences.
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Key words
Forecasting,Predictive models,Data models,Probabilistic logic,Mathematical models,Estimation,Time series analysis,Probabilistic forecasting,event prediction,temporal point processes,time-series,variational auto-encoder,ordinary differential equations,conditional variational learning
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