# Residual Entropy

## TL;DR

This paper introduces an entropy-based approach to improve model fitting by relaxing the assumption of uncorrelated residuals, leading to better generalization especially for ordered data sequences.

## Contribution

It proposes a novel entropy prior on residuals and extends the MSE loss to incorporate this, enhancing model fitting and avoiding overfitting.

## Key findings

- Simulations show overfitting signatures in residual autocorrelation.
- Entropy prior improves residual modeling in ordered sequences.
- Extended MSE loss can be applied immediately in practice.

## Abstract

We describe an approach to improving model fitting and model generalization that considers the entropy of distributions of modelling residuals. We use simple simulations to demonstrate the observational signatures of overfitting on ordered sequences of modelling residuals, via the autocorrelation and power spectral density. These results motivate the conclusion that, as commonly applied, the least squares method assumes too much when it assumes that residuals are uncorrelated for all possible models or values of the model parameters. We relax these too-stringent assumptions in favour of imposing an entropy prior on the (unknown, model-dependent, but potentially marginalizable) distribution function for residuals. We recommend a simple extension to the Mean Squared Error loss function that approximately incorporates this prior and can be used immediately for modelling applications where meaningfully-ordered sequences of observations or training data can be defined.