Recognizing changing seasonal patterns using neural networks
In this paper we propose a graphical method based on an artificial neural network model to investigate how and when seasonal patterns in macroeconomic time series change over time. Neural networks are useful since the hidden layer units may become activated only in certain seasons or periods, and since this activity can be stepwise or smooth. The graphical method is based on the partial contribution of the hidden layer units to the overall fit. We apply our method to quarterly Industrial Production in France and Netherlands.
|Keywords||neural networks, pattern recognition, seasonality|
|JEL||Time-Series Models; Dynamic Quantile Regressions (jel C22)|
|Persistent URL||dx.doi.org/10.1016/S0304-4076(97)00047-X, hdl.handle.net/1765/2107|
|Journal||Journal of Econometrics|
Franses, Ph.H.B.F, & Draisma, G. (1997). Recognizing changing seasonal patterns using neural networks. Journal of Econometrics, 273–280. doi:10.1016/S0304-4076(97)00047-X