Weather Predication Using Classification Methods: An Experimental
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Data Mining, Classification, Prediction, Time Series Data
Abstract
Weather data and specifically rainfall data contain streams of data that are collected hourly. These data can be used to identify the signatures of rivers and other areas. However, the process of discovering patterns from thousands of data information loss. Hence, precise data representation and pattern detection are crucial in ensuring that the patterns obtained have real meaning and can describe the weather signatures of certain areas accurately. In this paper, an experiment is carried out on classified rainfall sequences by case-based reasoning (CBR), with two other effective methods: a support vector machine (SVM) and a naïve Bayes (NB). The experimental results show that the incorporation of CBR gives both algorithms the capability to detect patterns of segmented rainfall with high accuracy and that SVM with CBR outperforms NB with CBR.
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