The research investigates the data mining technique K-Nearest Neighbor resulting in a predictor for numerical series. The series experimented with come from the climatic data usually hard to forecast due to uncertainty. One approach of prediction is to spot patterns in the past, when it is known in advance what followed them and verify it on more recent data. If a pattern is followed by the same outcome frequently enough, it can be concluded that it is a genuine relationship. Because this approach does not assume any special knowledge or form of the regularities, the method is quite general applicable to other series not just climate. The research searches for an automated pattern spotting, it involves data mining technique K-Nearest Neighbor for prediction of temperature and humidity data for a specific region. The results of the research for temperature and humidity prediction by K-Nearest Neighbor were satisfactory as it is assumed that no forecasting technique can be 100 % accurate in prediction.

Data Mining K-Nearest Neighbor Numerical Series