Ensemble Forecasting for Complex Time Series Using Sparse Representation and Neural Networks

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• 作者：Lean Yu ; Yang Zhao and Ling Tang
• 刊名：Journal of Forecasting
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：36
• 期：2
• 页码：122-138
• 全文大小：2015K
• ISSN：1099-131X

文摘

Based on the concept of ‘decomposition and ensemble’, a novel ensemble forecasting approach is proposed for complex time series by coupling sparse representation (SR) and feedforward neural network (FNN), i.e. the SR-based FNN approach. Three main steps are involved: data decomposition via SR, individual forecasting via FNN and ensemble forecasting via a simple addition method. In particular, to capture various coexisting hidden factors, the effective decomposition tool of SR with its unique virtues of flexibility and generalization is introduced to formulate an overcomplete dictionary covering diverse bases, e.g. exponential basis for main trend, Fourier basis for cyclical (and seasonal) features and wavelet basis for transient actions, different from other techniques with a single basis. Using crude oil price (a typical complex time series) as sample data, the empirical study statistically confirms the superiority of the SR-based FNN method over some other popular forecasting models and similar ensemble models (with other decomposition tools). Copyright