Friday, December 14, 2007

Models and simulations of consumers and rhythms of everyday life

Our Kulta project organized a seminar on 13th of December at the Helsinki University of Technology. Mika Pantzar, professor at National Consumer Research Center and researcher at Helsinki School of Economics, showed interesting examples of rhythms of everyday life. The topic is related to his collaboration research with Prof. Elisabeth Shove from Lancaster University.
Pantzar presented the idea of considering everyday life through the concepts of melody (sequentiality), harmony (synchronicity) and rhythm. He showed interesting examples of various kinds of time series. For instance, the time used for eating in Spain and France is clearly clustered around lunch and dinner times whereas in Finland people are eating throughout the day rather evenly.

Dr. Amaury Lendasse, the head of the group on Time series prediction and chemoinformatics, gave a tutorial on time series analysis. He presented a number of examples in the areas of economics, physics, industry, astronomy, climatology and hydrology. Lendasse made a distinction between recursive prediction, direct prediction and hybrid prediction in long term time series prediction. He also discussed links between variable selection, scaling selection and distance measures.

In the end, we discussed some similarities and differences between various disciplines that consider future. Time series prediction aims at developing methods for predicting the future values of some numerical variables. On the other hand, scenarios are created from a qualitative point of view. Similarly, control aims at manipulating some variable values in such a way that some quantitatively measurable process can be directed into wanted direction. Decision making aims at directing some process into wanted direction through measaures that can be described mainly at qualitative level.

1 comment:

Carl said...

Other good consumer demand data that shows nice cycles is water demand and natural gas / power demand, probably easily gotten on request from various power and utility companies around. Nice 2 "hump" data in the USA, with variances by day of week, including noticable differences on Saturdays and Sundays from the rest (of course).