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中文摘要：
针对多元混沌时间序列的预测问题,考虑到单纯改进储备池算法无法明显地提高预测精度,提出一种基于误差补偿的时间序列混合预测模型.实际观测的数据既包含线性特征又包含非线性特征.首先利用自回归移动平均模型预测线性特征,使得残差数据仅含非线性特征;然后,建立正则化回声状态网络模型预测;最后,将非线性部分的预测值与线性部分的预测值相加,以实现高精度的多元混沌时间序列预测.基于Lorenz和太阳黑子-黄河径流量时间序列的仿真实验验证了本文所提模型的有效性.
英文摘要：
Considering the problem that simply modifying the reservoir algorithm cannot significantly improve the prediction accuracy of chaotic multivariate time series, in this paper we propose a hybrid prediction model based on error correction. The observed data includes both linear and nonlinear features. First, we use autoregressive and moving average model to capture the linear features, then build a regularized echo state network to portray the dynamic nonlinear features. Finally, we add the predicted nonlinear value to the predicted linear value, in order to improve forecasting accuracy achieved by either of the models used separately. The experimental results of Lorenz and Sunspot-Runoff in the Yellow River time series demonstrate the effectiveness and characteristics of the proposed model herein.
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Sparse kernel density estimations and its application in variable selection based on quadratic Renyi
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