%0 Journal Article %1 Ilonen2006/2 %A Ilonen, Jarmo %A Kamarainen, Joni-Kristian %A Puumalainen, Kaisu %A Sundqvist, Sanna %A Kalviainen, Heikki %D 2006/2 %J Technological Forecasting and Social Change %K (SOM); Clustering Diffusion; Forecast; Innovation; Neural Self-organizing map networks; %N 2 %P 182-198 %T Toward automatic forecasts for diffusion of innovations %V 73 %X The paper presents an automated framework for forecasting the diffusion of innovations. The framework utilizes existing diffusion information from any market areas or similar products introduced to the markets earlier. The existing data, be it little, enormous, or not present at all, defines a corresponding decision path in the model, and following the path generates a forecast by maximizing the available information. An information-processing technique called a self-organizing map, SOM, was used to generate a map of the economic, technological and social market characteristics that have been found to affect diffusion. This map is used as a basis for finding suitable analogies for predicting the diffusion of an innovation in a specific market. The framework is applied in the context of predicting the diffusion of cellular subscriptions and Internet use worldwide and, separately, in the European Union, including the new member states. In the experiments the model yielded significantly better results than a regression using the Bass model. The method allows analysts to concentrate on more qualitative issues and the system to perform complicated computing operations. Furthermore, the system is self-refining since its accuracy continuously improves when new and more up-to-date information is added to the database. The proposed framework and methods aim to move present theory toward more practical and automatic prediction tools for company analysts and diffusion researchers.

@article{Ilonen2006/2, abstract = {The paper presents an automated framework for forecasting the diffusion of innovations. The framework utilizes existing diffusion information from any market areas or similar products introduced to the markets earlier. The existing data, be it little, enormous, or not present at all, defines a corresponding decision path in the model, and following the path generates a forecast by maximizing the available information. An information-processing technique called a self-organizing map, SOM, was used to generate a map of the economic, technological and social market characteristics that have been found to affect diffusion. This map is used as a basis for finding suitable analogies for predicting the diffusion of an innovation in a specific market. The framework is applied in the context of predicting the diffusion of cellular subscriptions and Internet use worldwide and, separately, in the European Union, including the new member states. In the experiments the model yielded significantly better results than a regression using the Bass model. The method allows analysts to concentrate on more qualitative issues and the system to perform complicated computing operations. Furthermore, the system is self-refining since its accuracy continuously improves when new and more up-to-date information is added to the database. The proposed framework and methods aim to move present theory toward more practical and automatic prediction tools for company analysts and diffusion researchers.}, added-at = {2008-08-31T18:03:07.000+0200}, author = {Ilonen, Jarmo and Kamarainen, Joni-Kristian and Puumalainen, Kaisu and Sundqvist, Sanna and Kalviainen, Heikki}, biburl = {https://www.bibsonomy.org/bibtex/2b63def910bda92bb2da98b3bec0a0dae/jomiralb}, description = {Old biblio}, interhash = {1bb7b2e696ad4b632f2ad0b72d8fb307}, intrahash = {b63def910bda92bb2da98b3bec0a0dae}, journal = {Technological Forecasting and Social Change}, keywords = {(SOM); Clustering Diffusion; Forecast; Innovation; Neural Self-organizing map networks;}, number = 2, owner = {oriol}, pages = {182-198}, timestamp = {2008-08-31T18:03:15.000+0200}, title = {Toward automatic forecasts for diffusion of innovations}, volume = 73, year = {2006/2} }