Long-range prediction of retail sales using recurrent radial basis function neural network Online publication date: Thu, 25-Jun-2015
by Minakhi Rout; Babita Majhi
International Journal of Foresight and Innovation Policy (IJFIP), Vol. 10, No. 1, 2015
Abstract: The literature survey on sales forecasting reveals that few works have been reported on long-range forecasting of sale volumes. On the other hand, there is a need of such long-range forecasting of sales data to devise suitable organisational strategy. The existing soft computing-based forecasting models provide poor prediction performance. Keeping this in view a new soft computing model is developed and utilised for prediction of seasonally adjusted (SA) and non-seasonally adjusted (NSA) sales volumes up to 24 months. The simulation results of real-life data show an excellent prediction performance compared to that of four other contemporary soft computing models.
Online publication date: Thu, 25-Jun-2015
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Foresight and Innovation Policy (IJFIP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org