# A Comparison of Prediction Algorithms and Nexting for Short Term Weather Forecasts

## TL;DR

This paper compares various supervised learning algorithms, including Nexting, neural networks, and regression trees, for short-term weather forecasting, highlighting that the choice of algorithm depends on data characteristics and side information.

## Contribution

It evaluates the performance of different regression algorithms on weather data, demonstrating that Nexting performs well with slowly varying signals and sufficient training data.

## Key findings

- Nexting performs well with slowly varying signals.
- Algorithm choice depends on available side information.
- No single method is clearly superior across all cases.

## Abstract

This report first provides a brief overview of a number of supervised learning algorithms for regression tasks. Among those are neural networks, regression trees, and the recently introduced Nexting. Nexting has been presented in the context of reinforcement learning where it was used to predict a large number of signals at different timescales. In the second half of this report, we apply the algorithms to historical weather data in order to evaluate their suitability to forecast a local weather trend. Our experiments did not identify one clearly preferable method, but rather show that choosing an appropriate algorithm depends on the available side information. For slowly varying signals and a proficient number of training samples, Nexting achieved good results in the studied cases.