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原文連結
論文資訊
- 類型：已發表論文
- 日期：2015-12-01
摘要
Prediction models that capture and use the structure of state-space dynamics can be very effective. In practice, however, one rarely has access to full 資訊 about that structure, and accurate reconstruction of the dynamics from scalar time-series data-e.g., via delay-coordinate embedding-can be a real challenge. In this paper, we show that forecast models that employ incomplete reconstructions of the dynamics-i.e., models that are not necessarily true embeddings-can produce surprisingly accurate predictions of the state of a dynamical system. In particular, we demonstrate the effectiveness of a simple near-neighbor forecast technique that works with a two-dimensional time-delay reconstruction of both low-and high-dimensional dynamical systems. Even though correctness of the topology may not
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