Target tracking techniques have usually been applied to physical systems via radar, sonar or imaging modalities.
But the same techniques - filtering, association, classification, track management - can be applied to nontraditional
data such as one might find in other fields such as economics, business and national defense. In this
paper we explore a particular data set. The measurements are time series collected at various sites; but other
than that little is known about it. We shall refer to as the data as representing the Megawatt hour (MWH)
output of various power plants located in Afghanistan. We pose such questions as: 1. Which power plants seem to have a common model?
2. Do any power plants change their models with time?
3. Can power plant behavior be predicted, and if so, how far to the future?
4. Are some of the power plants stochastically linked? That is, do we observed a lack of power demand at
one power plant as implying a surfeit of demand elsewhere?
The observations seem well modeled as hidden Markov. This HMM modeling is compared to other approaches;
and tests are continued to other (albeit self-generated) data sets with similar characteristics.
Keywords: Time-series analysis, hidden Markov models, statistical similarity, clustering weighted