计算机科学
非线性系统
系列(地层学)
水准点(测量)
时间序列
时域
人工智能
领域(数学分析)
序列(生物学)
频域
小波变换
人工神经网络
数据挖掘
模式识别(心理学)
算法
机器学习
作者
Lifan Long,Qian Liu,Hong Peng,Qian Yang,Xiaohui Luo,Jun Wang,Xiaoxiao Song
标识
DOI:10.1142/s0129065722500204
摘要
Nonlinear spiking neural P (NSNP) systems are a recently developed theoretical model, which is abstracted by nonlinear spiking mechanism of biological neurons. NSNP systems have a nonlinear structure and the potential to describe nonlinear dynamic systems. Based on NSNP systems, a novel time series forecasting approach is developed in this paper. During the training phase, a time series is first converted to frequency domain by using a redundant wavelet transform, and then according to the frequency data, an NSNP system is automatically constructed and adaptively trained in frequency domain. Then, the well-trained NSNP system can automatically generate sequence data for future time as the prediction results. Eight benchmark time series data sets and two real-life time series data sets are utilized to compare the proposed approach with several state-of-the-art forecasting approaches. The comparison results demonstrate availability and effectiveness of the proposed forecasting approach.
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