| dc.contributor.author | Kihoro, J | |
| dc.contributor.author | Otieno, Romanus Odhiambo | |
| dc.contributor.author | Wafula, C | |
| dc.date.accessioned | 2018-11-20T15:04:29Z | |
| dc.date.accessioned | 2020-02-06T14:01:25Z | |
| dc.date.available | 2018-11-20T15:04:29Z | |
| dc.date.available | 2020-02-06T14:01:25Z | |
| dc.date.issued | 2004 | |
| dc.identifier.citation | Kihoro, J., Otieno, R., & Wafula, C. (2004). Seasonal time series forecasting: A comparative study of ARIMA and ANN models. AJST, 5(2). | en_US |
| dc.identifier.uri | https://pdfs.semanticscholar.org/123a/f7e5b9dd2171826ca4f3df50406d9df4459a.pdf | |
| dc.identifier.uri | http://repository.must.ac.ke/handle/123456789/978 | |
| 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.publisher | African Journal of Science and Technology (AJST) | en_US |
| 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 |
