Efficient sales forecasting using PSO based adaptive ARMA model
| dc.contributor.author | Majhi R. | en_US |
| dc.contributor.author | Mishra S. | en_US |
| dc.contributor.author | Majhi B. | en_US |
| dc.contributor.author | Panda G. | en_US |
| dc.contributor.author | Rout M. | en_US |
| dc.date.accessioned | 2025-02-11T12:19:31Z | |
| dc.date.issued | 2009 | |
| dc.description.abstract | The paper proposes a new hybrid forecasting model using auto regressive moving average (ARMA) as basic architecture and particle swarm optimization (PSO) as learning algorithm. These two combinations have yielded an efficient prediction model for retail sales volumes. To facilitate comparison ARMA | en_US |
| dc.description.abstract | functional link artificial neural network (FLANN) and MLP models are also simulated. The performance of the new model has been evaluated through simulation study and the results demonstrate the best prediction performance both for long and short ranges. �2009 IEEE. | en_US |
| dc.identifier.uri | http://dx.doi.org/10.1109/NABIC.2009.5393738 | |
| dc.identifier.uri | https://idr.iitbbs.ac.in/handle/2008/18 | |
| dc.language.iso | en | en_US |
| dc.subject | Adaptive auto regressive moving average (ARMA) model and particle swarm optimization (PSO) | en_US |
| dc.subject | Sales forecasting | en_US |
| dc.title | Efficient sales forecasting using PSO based adaptive ARMA model | en_US |
| dc.type | Conference Paper | en_US |