SEASONAL TIME SERIES FORECASTING: A COMPARATIVE STUDY OF ARIMA AND ANN MODELS

dc.contributor.author	Kihoro, J.M.
dc.contributor.author	Otieno, R.O.
dc.contributor.author	Wafula, C.
dc.date.accessioned	2017-04-26T16:23:27Z
dc.date.available	2017-04-26T16:23:27Z
dc.date.issued	2004-12
dc.identifier.uri	http://hdl.handle.net/123456789/203
dc.description.abstract	This paper addresses the concerns of Faraway and Chatfield (1998) who questioned the forecasting ability of Artificial Neural Networks (ANN). In particular the paper compares the performance of Artificial Neural Networks (ANN) and ARIMA models in forecasting of seasonal (monthly) Time series. Using the Airline data which Faraway and Chatfield (1998) used and two other data sets and taking into consideration their suggestions, we show that ANN are not as bad as Faraway and Chatfield put it. A rule of selecting input lags into the input set based on their relevance/ contribution to the model is also proposed.	en_US
dc.language.iso	en	en_US
dc.relation.ispartofseries	;Vol. 5, No. 2,
dc.subject	Time Series; Seasonal Autoregressive Integrated Moving Average (SARIMA); Artificial Neural Network (ANN); Multilayered Perceptrons (MLP); Time lagged Neural Networks (TLNN); Automatic Relevance Determination (ARD)	en_US
dc.title	SEASONAL TIME SERIES FORECASTING: A COMPARATIVE STUDY OF ARIMA AND ANN MODELS	en_US
dc.type	Article	en_US