Title :
Predicting the impact of advertising: a neural network approach
Author :
Johansson, Ulf ; Niklasson, Lars
Author_Institution :
Dept. of Bus. & Inf., Boras Univ., Sweden
fDate :
6/23/1905 12:00:00 AM
Abstract :
This paper studies if neural networks using the temporal structure of the domain can raise the accuracy when predicting the outcome of investments in advertising (both on monthly and yearly basis), compared to the methods used today. The focus has been to investigate if future publicity can be predicted from historical outcome and planned future media investments. The domain is the car industry. This paper contains a case study where ANNs utilize time series effects for accurate prediction (the effect of advertising has a temporal signature). It is a comparative study between different network architectures that conclusively show that sequential and recurrent approaches exploit the time series dependencies and yield a performance, which supersede approaches traditionally used
Keywords :
advertising data processing; forecasting theory; investment; neural nets; time series; advertising; car industry; investments; neural network; outcome prediction; time series; Accuracy; Advertising; Computer science; Informatics; Investments; Neural networks; Predictive models;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.938435