Trend time series Modeling and forecasting with neural networks

被引：6

作者：

Qi, M ^{[1
]}

Zhang, GP ^{[1
]}

机构：

[1] Kent State Univ, Dept Econ, Kent, OH 44242 USA

来源：

2003 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR FINANCIAL ENGINEERING, PROCEEDINGS | 2003年

关键词：

neural networks; trend time series; forecasting; trend-stationary; difference-stationary;

D O I：

10.1109/CIFER.2003.1196279

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

Despite its great importance, there has been no general consensus on how to model the trends in time series data Compared to traditional approaches, neural networks have shown some promise in time series forecasting. This paper investigates how to best model trend time series using neural networks. Four strategies (raw data, raw data with time index, detrending, and differencing) are used to model various simulated trend patterns (linear, nonlinear, deterministic, stochastic, and breaking trend). We find that with neural networks differencing often gives meritorious results regardless of the underlying DGPs. This finding is also confirmed by the real GNP series.

引用

页码：331 / 337

页数：7

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