A stochastic jitter model for analyzing digital timing-recovery circuits
Proceedings of the 46th Annual Design Automation Conference
Robust curve clustering based on a multivariate t-distribution model
IEEE Transactions on Neural Networks
Neural architectures for global solar irradiation and air temperature prediction
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Bayesian variable selection in neural networks for short-term meteorological prediction
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Time series prediction method based on LS-SVR with modified gaussian RBF
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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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.