Regime Shifts in Streams: Real-time Forecasting of Co-evolving Time Sequences

author: Yasuko Matsubara, Graduate School of Science and Technology, Kumamoto University
published: Sept. 27, 2016,   recorded: August 2016,   views: 1680
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Description

Given a large, online stream of multiple co-evolving event sequences, such as sensor data and Web-click logs, that contains various types of non-linear dynamic evolving patterns of different durations, how can we efficiently and effectively capture important patterns? How do we go about forecasting long-term future events?

In this paper, we present REGIMECAST, an efficient and effective method for forecasting co-evolving data streams. REGIME-CAST is designed as an adaptive non-linear dynamical system, which is inspired by the concept of “regime shifts” in natural dynamical systems. Our method has the following properties: (a) Effective: it operates on large data streams, captures important patterns and performs long-term forecasting; (b) Adaptive: it automatically and incrementally recognizes the latent trends and dynamic evolution patterns (i.e., regimes) that are unknown in advance; (c) Scalable: it is fast and the computation cost does not depend on the length of data streams; (d) Any-time: it provides a response at any time and generates long-range future events.

Extensive experiments on real datasets demonstrate that REGIME-CAST does indeed make long-range forecasts, and it outperforms state-of-the-art competitors as regards accuracy and speed.

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