Multivariate time series forecasting remains a challenging task because of its nonlinear, non-
stationary, high-dimensional, and spatial–temporal characteristics, along with the …

摘要复杂系统产生的混沌时间序列普遍存在于天文, 水文, 气象, 环境, 金融等领域.
混沌时间序列的分析与预测对于理解复杂系统特性, 探究系统演化规律具有重要作用 …

In this paper, energy consumption of an office building is predicted based on echo state
networks (ESNs). Energy consumption of the office building is divided into consumptions …

Motivated by the idea of'decomposition and ensemble', this paper proposes a novel method
based on the wavelet-denoising algorithm and multiple echo state networks to improve the …

Echo state networks (ESNs) have been widely used in the field of time series prediction. In
conventional ESNs, the spectral radius of reservoir is always scaled to lower than 1 to satisfy …

Kernel adaptive filter (KAF) has been widely utilized for time series prediction due to its
online adaptation scheme, universal approximation capability and convexity. Nevertheless …

Nonlinear spiking neural P (NSNP) systems are variants of neural-like membrane computing
models, abstracted by nonlinear spiking mechanisms of biological neurons. NSNP systems …

Fuzzy functions (FFs) models were introduced as an alternate representation of the fuzzy
rule based approaches. This paper presents novel Interactively Recurrent Fuzzy Functions …

Considering that real-life time series mixed with missing points cannot be directly modeled
by using most of the supervised machine learning methods, this paper proposes a novel …

摘要针对传统多变量时间序列预测方法未考虑变量间依赖关系从而影响预测效果的问题,
提出了一种基于异常序列剔除的多变量时间序列预测算法. 该算法旨在利用多维支持向量回归机 …