平均绝对百分比误差
均方误差
人工神经网络
时间序列
自回归积分移动平均
自回归模型
计算机科学
希尔伯特-黄变换
系列(地层学)
自回归滑动平均模型
计量经济学
波动性(金融)
移动平均线
奇异谱分析
统计
人工智能
机器学习
数学
奇异值分解
白噪声
生物
古生物学
标识
DOI:10.1016/j.eswa.2016.02.025
摘要
The empirical mode decomposition (EMD) has been successfully applied to adaptively decompose economic and financial time series for forecasting purpose. Recently, the variational mode decomposition (VMD) has been proposed as an alternative to EMD to easily separate tones of similar frequencies in data where the EMD fails. The purpose of this study is to present a new time series forecasting model which integrates VMD and general regression neural network (GRNN). The performance of the proposed model is evaluated by comparing the forecasting results of VMD-GRNN with three competing prediction models; namely the EMD-GRNN model, feedforward neural networks (FFNN), and autoregressive moving average (ARMA) process on West Texas Intermediate (WTI), Canadian/US exchange rate (CANUS), US industrial production (IP) and the Chicago Board Options Exchange NASDAQ 100 Volatility Index (VIX) time series are used for experimentations. Based on mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean of squared errors (RMSE), the analysis results from forecasting demonstrate the superiority of the VMD-based method over the three competing prediction approaches. The practical analysis results suggest that VMD is an effective and promising technique for analysis and prediction of economic and financial time series.
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