# Algorithmic Information Forecastability

arXiv: https://arxiv.org/abs/2304.10752 · HTML: https://mlsanity.com/paper/2304.10752/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 22 references that are in the map.
- First posted on arXiv 21 Apr 2023.

## Abstract (arXiv)

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.