A Comparison of Prediction Algorithms and Nexting for Short Term Weather Forecasts
This work addresses the problem of selecting appropriate prediction algorithms for short-term weather forecasts, but it is incremental as it applies existing methods to a new domain without introducing major innovations.
The paper compared several supervised learning algorithms, including neural networks, regression trees, and Nexting, for short-term weather forecasting using historical data, finding that no single method was clearly best and that algorithm choice depends on available side information, with Nexting performing well for slowly varying signals with sufficient training samples.
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.