Oil Price Trackers Inspired by Immune Memory

TL;DR

This paper proposes an immune-inspired algorithm that dynamically evolves oil price trackers to predict future trends and analyze market properties, offering a novel approach to time series forecasting.

Contribution

It introduces a new immune-inspired method for evolving and managing trackers for oil price prediction, combining short-term adaptation with long-term memory.

Findings

Trackers successfully predict oil price movements.

The approach provides insights into crude oil market dynamics.

Dynamic tracker evolution enhances forecasting accuracy.

Abstract

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.