Authors:
Ibrahim Zafar Qureshi
1
;
Marwan Khammash
2
and
Konstantinos Nikolopoulos
2
Affiliations:
1
Institute Risk Analyst and Allied Bank Limited, Pakistan
;
2
Bangor Business School, United Kingdom
Keyword(s):
Forecasting, Promotions, Marketing, Artificial Neural Networks.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Formal Methods
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial Applications of AI
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Planning and Scheduling
;
Sensor Networks
;
Signal Processing
;
Simulation and Modeling
;
Soft Computing
;
Symbolic Systems
;
Theory and Methods
Abstract:
In this study we model the effect of promotions in time-series data and we consequently forecast that extraordinary effect via Artificial Neural Networks (ANN) as implemented from the Expert Method of a popular Artificial Intelligence software. We simulate data considering five factors as to determine the actual impact of each individual promotion. We consider additive and multiplicative models, with the later presenting both linear and non-linear relationships between those five factors; in addition, we superimpose either low or high levels of noise. Our empirical findings suggest that, for nonlinear models with high level of noise, ANN outperform all benchmarks. Standard ANN topologies work well for models with up to two factors while the Expert method provided by the software works well for higher number of factors.