Title :
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
fDate :
5/1/2008 12:00:00 AM
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;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.912308
