Forecast Ergodicity: Prediction Modeling Using Algorithmic Information Theory

04/21/2023
by   Glauco Amigo, et al.
0

The capabilities of machine intelligence are bounded by the potential of data from the past to forecast the future. Deep learning tools are used to find structures in the available data to make predictions about the future. Such structures have to be present in the available data in the first place and they have to be applicable in the future. Forecast ergodicity is a measure of the ability to forecast future events from data in the past. We model this bound by the algorithmic complexity of the available data.

READ FULL TEXT

page 5

page 8

research
01/19/2007

Algorithmic Complexity Bounds on Future Prediction Errors

We bound the future loss when predicting any (computably) stochastic seq...
research
09/04/2019

Mape_Maker: A Scenario Creator

We describe algorithms for creating probabilistic scenarios for the situ...
research
09/10/2022

On the Evaluation of Skill in Binary Forecast

A good prediction is very important for scientific, economic, and admini...
research
07/20/2022

The Forecast Trap

Encouraged by decision makers' appetite for future information on topics...
research
07/29/2023

Rapid Flood Inundation Forecast Using Fourier Neural Operator

Flood inundation forecast provides critical information for emergency pl...
research
05/26/2020

Using Machine Learning to Forecast Future Earnings

In this essay, we have comprehensively evaluated the feasibility and sui...
research
07/15/2022

The Transform-o-meter: A method to forecast the transformative impact of innovation

With the advent of Transformative Artificial Intelligence, it is now mor...

Please sign up or login with your details

Forgot password? Click here to reset