Sequential Pattern Discovery Algorithm for Malaysia Rainfall Prediction
A.M. Ahmeda, A.A. Bakara, A.R. Hamdana, S.M. Syed Abdullahb and O. Jaafarb
aCenter for Artificial Intelligence Technology, Faculty of Information Science and Technology
bInstitute of Climate Change, University Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan, Malaysia
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This study proposes a sequential pattern mining algorithm to discover sequential patterns of Malaysia rainfall data for prediction. The apriori based algorithm is employed to find the sequential patterns from the time series data. The frequent episodes of rainfall sequences are discovered and classified by the expert into four main events namely, No rain, Light, Moderate and heavy. The sequential rules of ten rainfall stations from the duration of 33 years are analysed. The proposed algorithm is able to generate higher confidence and support of frequent and sequential patterns. Generally, the proposed study has shown its potential in producing methods that manage to preserve important knowledge and thus reduce information loss in weather prediction problem.

DOI: 10.12693/APhysPolA.128.B-324
PACS numbers: 92.40.Zg, 92.60.Wc