Multivariate Chaotic Time Series Online Prediction Based on Improved Kernel Recursive Least Squares Algorithm
提出一种改进的核递归最小二乘算法，结合近似线性依赖、动态调整和相干性准则，用于多元混沌时间序列的在线预测，在洛伦兹、厄尔尼诺、太阳黑子和黄河径流数据上验证了有效性。
Kernel recursive least squares (KRLS) is a kind of kernel methods, which has attracted wide attention in the research of time series online prediction. It has low computational complexity and updates in a recursive form. However, as data size increases, computational complexity of calculating kernel inverse matrix will raise. And it has some difficulties in accommodating time-varying environments. Therefore, we have presented an improved KRLS algorithm for multivariate chaotic time series online prediction. Approximate linear dependency, dynamic adjustment, and coherence criterion are combined with quantization to form our improved KRLS algorithm. In the process of online prediction, it can bring computational efficiency up and adjust weights adaptively in time-varying environments. Moreover, Lorenz chaotic time series, El Nino-Southern Oscillation indexes chaotic time series, yearly sunspots and runoff of the Yellow River chaotic time series online prediction are presented to prove the effectiveness of our proposed algorithm.