Accepted Papers

Dynamic Modeling and Forecasting of Time-evolving Data Streams

Yasuko Matsubara (Kumamoto University);Yasushi Sakurai (Kumamoto University);


Given a large, semi-infinite collection of co-evolving data sequences (e.g., IoT/sensor streams), which contains multiple distinct dynamic time-series patterns, our aim is to incrementally monitor current dynamic patterns and forecast future behavior. We present an intuitive model, namely OrbitMap, which provides a good summary of time-series evolution in streams. We also propose a scalable and effective algorithm for fitting and forecasting time-series data streams. Our method is designed as a dynamic, interactive and flexible system, and is based on latent non-linear differential equations. Our proposed method has the following advantages: (a) It is effective: it captures important time-evolving patterns in data streams and enables real-time, long-range forecasting; (b) It is general: our model is general and practical and can be applied to various types of time-evolving data streams; (c) It is scalable: our algorithm does not depend on data size, and thus is applicable to very large sequences. Extensive experiments on real datasets demonstrate that OrbitMap makes long-range forecasts, and consistently outperforms the best existing state-of-the-art methods as regards accuracy and execution speed.

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