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arXiv · 2304.10752

Algorithmic Information Forecastability

Glauco Amigo·Daniel Andrés Díaz-Pachón·Robert J. Marks·Charles Baylis
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Abstract

The outcome of all time series cannot be forecast, e.g. the flipping of a fair coin. Others, like the repeated {01} sequence {010101...} can be forecast exactly. Algorithmic information theory can provide a measure of forecastability that lies between these extremes. The degree of forecastability is a function of only the data. For prediction (or classification) of labeled data, we propose three categories for forecastability: oracle forecastability for predictions that are always exact, precise forecastability for errors up to a bound, and probabilistic forecastability for any other predictions. Examples are given in each case.

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BibTeXRIS

Glauco Amigo, Daniel Andrés Díaz-Pachón, Robert J. Marks, Charles Baylis. 2023-04-21. Algorithmic Information Forecastability. https://arxiv.org/abs/2304.10752

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