Weather Forecasting Using Memory-based Reasoning

We have implemented a weather forecasting system, WINDOM, which uses memorybased reasoning. The observation data from Japan Meteorological Agency's networks were used directly as input data and the system was used to predict the weather. We used 5 categories such as clear, clouded, rainy, snowy, stormy, or 2 categories such as rainy or not rainy, to describe Tokyo 6 hours ahead. Two weighting methods of features, per category importance and cross category importance, were tested. The per category importancemethod, the more precise one, turned out to be too sensitive for the proportion of the answer categories and performed worse than cross category importance method. In the experiments, a hit rate of 84.1% was achieved, where the task was to predict if the weather 6 hours ahead would be rainy or not rainy.