Time series change detection using reservoir computing networks for remote sensing data

In this study, a multivariate time series forecasting model applicable to high-dimensional short-term forecasting is proposed. The Reservoir Computing Network (RCN) state reduction method is investigated to address the dimensionality arising from high-dimensional data. To enable the states to express very distant dependencies in time so that the RCN satisfies the echo state property, a bidirectional RCN model is investigated to capture the dependencies of the data forward and backward in time. To solve the short-term data prediction, the spatiotemporal transformation theory is combined with the RCN to exploit the conjugate property between delayed and nondelayed embeddings of dynamical systems, and the correlation between variables in high-dimensional systems is used to compensate for the shortcomings of short-term series, design the method of the RCN training, and complete the prediction of future states. In the experiments, four existing models based on two different data sets were used for comparison, and the results showed that our method has better prediction performance.

Automated summary

In this study, a multivariate time series forecasting model applicable to high-dimensional short-term forecasting is proposed. The Reservoir Computing Network (RCN) state reduction method is investigated to address the dimensionality arising from high-dimensional data. A bidirectional RCN model is investigated to capture the dependencies of the data forward and backward in time. The combination of spatiotemporal transformation theory and RCN, along with the correlation between variables in high-dimensional systems, compensates for the shortcomings of short-term series and improves prediction performance.