Algorithmic Information Forecastability

• URL: http://arxiv.org/abs/2304.10752v2
• Date: Fri, 1 Dec 2023 16:54:49 GMT
• Title: Algorithmic Information Forecastability
• Authors: Glauco Amigo, Daniel Andr\'es D\'iaz-Pach\'on, Robert J. Marks, Charles Baylis
• Abstract summary: degree of forecastability is a function of only the data. oracle forecastability for predictions that are always exact, precise forecastability for errors up to a bound, and probabilistic forecastability for any other predictions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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