  We outline initial concepts for an immune inspired algorithm to evaluate and
predict oil price time series data. The proposed solution evolves a short term
pool of trackers dynamically, with each member attempting to map trends and
anticipate future price movements. Successful trackers feed into a long term
memory pool that can generalise across repeating trend patterns. The resulting
sequence of trackers, ordered in time, can be used as a forecasting tool.
Examination of the pool of evolving trackers also provides valuable insight
into the properties of the crude oil market.
