Using String Invariants for Prediction Searching for Optimal Parameters

TL;DR

This paper introduces a new prediction method based on string invariants that does not require learning, utilizing an evolutionary algorithm for parameter optimization, and demonstrates competitive performance on various datasets.

Contribution

The paper presents a novel string invariants-based prediction method combined with evolutionary optimization, offering an alternative to traditional statistical and AI approaches.

Findings

Performs well in single-step prediction

Requires parameter tuning for optimal results

Needs improvement for multi-step prediction

Abstract

We have developed a novel prediction method based on string invariants. The method does not require learning but a small set of parameters must be set to achieve optimal performance. We have implemented an evolutionary algorithm for the parametric optimization. We have tested the performance of the method on artificial and real world data and compared the performance to statistical methods and to a number of artificial intelligence methods. We have used data and the results of a prediction competition as a benchmark. The results show that the method performs well in single step prediction but the methods performance for multiple step prediction needs to be improved. The method works well for a wide range of parameters.