Predicting diffusion of innovations with self-organisation and machine learning
Ilonen, Jarmo (2003)
Tiivistelmä
The main subject of this master's thesis was predicting diffusion of
innovations. The prediction was done in a special case: product has
been available in some countries, and based on its diffusion in those
countries the prediction is done for other countries. The prediction
was based on finding similar countries with Self-Organizing Map~(SOM),
using parameters of countries. Parameters included various economical
and social key figures. SOM was optimised for different products using
two different methods: (a) by adding diffusion information of products
to the country parameters, and (b) by weighting the country parameters
based on their importance for the diffusion of different products. A
novel method using Differential Evolution (DE) was developed to solve
the latter, highly non-linear optimisation problem.
Results were fairly good. The prediction method seems to be on a solid
theoretical foundation. The results based on country data were
good. Instead, optimisation for different products did not
generally offer clear benefit, but in some cases the improvement was
clearly noticeable. The weights found for the parameters of the
countries with the developed SOM optimisation method were interesting,
and most of them could be explained by properties of the products.
innovations. The prediction was done in a special case: product has
been available in some countries, and based on its diffusion in those
countries the prediction is done for other countries. The prediction
was based on finding similar countries with Self-Organizing Map~(SOM),
using parameters of countries. Parameters included various economical
and social key figures. SOM was optimised for different products using
two different methods: (a) by adding diffusion information of products
to the country parameters, and (b) by weighting the country parameters
based on their importance for the diffusion of different products. A
novel method using Differential Evolution (DE) was developed to solve
the latter, highly non-linear optimisation problem.
Results were fairly good. The prediction method seems to be on a solid
theoretical foundation. The results based on country data were
good. Instead, optimisation for different products did not
generally offer clear benefit, but in some cases the improvement was
clearly noticeable. The weights found for the parameters of the
countries with the developed SOM optimisation method were interesting,
and most of them could be explained by properties of the products.