Trend Time–Series Modeling and Forecasting With Neural Networks

Author

Qi, Min ; Zhang, G. Peter

Author_Institution

Office of the Comptroller of the Currency, Washington

Volume

19

Issue

5

fYear

2008

fDate

5/1/2008 12:00:00 AM

Firstpage

808

Lastpage

816

Abstract

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 (NNs) have shown some promise in time-series forecasting. This paper investigates how to best model trend time series using NNs. Four different strategies (raw data, raw data with time index, detrending, and differencing) are used to model various trend patterns (linear, nonlinear, deterministic, stochastic, and breaking trend). We find that with NNs differencing often gives meritorious results regardless of the underlying data generating processes (DGPs). This finding is also confirmed by the real gross national product (GNP) series.

Keywords

forecasting theory; neural nets; time series; data generating process; gross national product series; neural network; time series data; time series forecasting; trend pattern; trend time series modeling; Difference-stationary (DS) series; forecasting; neural networks (NNs); trend time series; trend-stationary (TS) series; Computer Simulation; Data Interpretation, Statistical; Forecasting; Linear Models; Models, Econometric; Monte Carlo Method; Neural Networks (Computer); Nonlinear Dynamics; Stochastic Processes; Time Factors;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2007.912308

Filename

4469947