Trend time-series modeling and forecasting with neural networks.
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.
Qi
Min
M
Risk Analysis Division, Office of the Comptroller of the Currency, Washington, DC 20219, USA. min.qi@occ.treas.gov
Zhang
G Peter
GP
D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005544:Forecasting / Q000379:methods*; D016014:Linear Models; D017059:Models, Econometric; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013269:Stochastic Processes; D013997:Time Factors