Predicting the Impact of Advertising : a Neural Network Approach

@inproceedings{Johansson2001PredictingTI,
  title={Predicting the Impact of Advertising : a Neural Network Approach},
  author={Ulf Johansson},
  year={2001}
}
The purpose of this study has been to evaluate 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… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-9 of 9 references

Finding Structure in Time

Cognitive Science • 1990
View 5 Excerpts
Highly Influenced

Roubi, "Artificial Neural networks vs Multiple Regression in Tourism Demand Analysis

M. Uysal, S. El
Journal of Travel Research, • 1999

Neural Networks for Time Series Processing

G. Dorffner
Neural Network World 4/96, • 1996
View 1 Excerpt

Bayesian interpolation

J. C. MacKay David
Neural Computation, • 1992
View 1 Excerpt

Predicting Sunspots and Exchange Rates with Connectionist Networks", Nonlinear Modeling and Forecasting, SFI Studies in the Sciences of Complexity

A. S. Weigend, B. A. Huberman, D. E. Rumelhart
Proceedings, vol. XII, • 1992

Similar Papers

Loading similar papers…