- 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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Collections
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details | SOURCEPDF | 15 MB | Adobe Acrobat PDF | View Details |