Learning Generative RNN-ODE for Collaborative Time-Series and Event Sequence Forecasting

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...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2023-07, Vol.35 (7), p.7118-7137
Main Authors: Li, Longyuan, Yan, Junchi, Zhang, Yunhao, Zhang, Jihai, Bao, Jie, Jin, Yaohui, Yang, Xiaokang
Format: Article
Language:English
Subjects:
Coders
Collaboration
conditional variational learning
Data collection
Data models
Decision making
Deep learning
Differential equations
Estimation
event prediction
Forecasting
Machine learning
Mathematical models
Ordinary differential equations
Predictive models
Probabilistic forecasting
Probabilistic logic
Recurrent neural networks
Sequences
temporal point processes
Time series
Time series analysis
variational auto-encoder
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Summary: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.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2022.3185115