✦ LIBER ✦

Sales forecasting using time series and neural networks

✍ Scribed by Angela P. Ansuj; M.E. Camargo; R. Radharamanan; D.G. Petry

Publisher: Elsevier Science
Year: 1996
Tongue: English
Weight: 217 KB
Volume: 31
Category: Article
ISSN: 0360-8352
DOI: 10.1016/0360-8352(96)00166-0

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Drought forecasting using artificial neu

Drought forecasting using artificial neural networks and time series of drought indices

✍ Saeid Morid; Vladimir Smakhtin; K. Bagherzadeh 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 230 KB

## Abstract Drought forecasting is a critical component of drought risk management. The paper describes an approach to drought forecasting, which makes use of Artificial Neural Network (ANN) and predicts quantitative values of drought indices—continuous functions of rainfall which measure the degre

Neural network forecasting of short, noi

Neural network forecasting of short, noisy time series

✍ W.R. Foster; F. Collopy; L.H. Ungar 📂 Article 📅 1992 🏛 Elsevier Science 🌐 English ⚖ 513 KB

Long-term sales forecasting using holt–w

Long-term sales forecasting using holt–winters and neural network methods

✍ Apostolos Kotsialos; Markos Papageorgiou; Antonios Poulimenos 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 242 KB

## Abstract The problem of medium to long‐term sales forecasting raises a number of requirements that must be suitably addressed in the design of the employed forecasting methods. These include long forecasting horizons (up to 52 periods ahead), a high number of quantities to be forecasted, which l

Time series forecasting using neural net

Time series forecasting using neural networks: should the data be deseasonalized first?

✍ Michael Nelson; Tim Hill; William Remus; Marcus O'Connor 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 118 KB 👁 1 views

This research investigates whether prior statistical deseasonalization of data is necessary to produce more accurate neural network forecasts. Neural networks trained with deseasonalized data from Hill et al. (1996) were compared with neural networks estimated without prior deseasonalization. Both s

Hybrid neural network models for hydrolo

Hybrid neural network models for hydrologic time series forecasting

✍ Ashu Jain; Avadhnam Madhav Kumar 📂 Article 📅 2007 🏛 Elsevier Science 🌐 English ⚖ 163 KB

Integrating time-series and end-use meth

Integrating time-series and end-use methods to forecast electricity sales

✍ Edward B. Fischler; Robert F. Nelson 📂 Article 📅 1986 🏛 John Wiley and Sons 🌐 English ⚖ 842 KB

Two types of forecasting methods have been receiving increasing attention by electric utility forecasters. The first type, called end-use forecasting, is recognized as an approach which is well suited for forecasting during periods characterized by technological change. The method is straightforward