An analysis of neural networks and time series techniques for demand forecasting

Winn, David

Title: An analysis of neural networks and time series techniques for demand forecasting
Creator: Winn, David
Subject: Time-series analysis
Subject: Neural networks (Computer science)
Subject: Artificial intelligence
Subject: Marketing -- Management
Subject: Marketing -- Data processing
Subject: Marketing -- Statistical methods
Subject: Consumer behaviour
Date: 2007
Type: Thesis
Type: Masters
Type: MCom
Identifier: vital:5572
Identifier: http://hdl.handle.net/10962/d1004362
Identifier: Time-series analysis
Identifier: Neural networks (Computer science)
Identifier: Artificial intelligence
Identifier: Marketing -- Management
Identifier: Marketing -- Data processing
Identifier: Marketing -- Statistical methods
Identifier: Consumer behaviour
Description: This research examines the plausibility of developing demand forecasting techniques which are consistently and accurately able to predict demand. Time Series Techniques and Artificial Neural Networks are both investigated. Deodorant sales in South Africa are specifically studied in this thesis. Marketing techniques which are used to influence consumer buyer behaviour are considered, and these factors are integrated into the forecasting models wherever possible. The results of this research suggest that Artificial Neural Networks can be developed which consistently outperform industry forecasting targets as well as Time Series forecasts, suggesting that producers could reduce costs by adopting this more effective method.
Format: 136 leaves, pdf
Publisher: Rhodes University, Faculty of Science, Statistics
Language: English
Rights: Winn, David

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