Affiliation:
1. Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai 200433 China
2. Research Institute of Intelligent Complex Systems Fudan University Shanghai 200433 China
Abstract
Accurate prediction of the future evolution of observational time series is a paramount challenge in current data‐driven research. While existing techniques struggle to learn useful representations from the temporal correlations, the high dimensionality in spatial domain is always considered as obstacle, leading to the curse of dimensionality and excessive resource consumption. This work designs a novel structure‐aware reservoir computing aiming at enhancing the predictability of coupled time series, by incorporating their historical dynamics as well as structural information. Paralleled reservoir computers with redesigned mixing inputs based on spatial relationships are implemented to cope with the multiple time series, whose core idea originates from the principle of the celebrated Granger causality. Representative numerical simulations and comparisons demonstrate the superior performance of the approach over the traditional ones. This work provides valuable insights into deeply mining both temporal and spatial information to enhance the representation learning of data in various machine learning techniques.
Funder
National Natural Science Foundation of China
Science and Technology Commission of Shanghai Municipality
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献