A Comprehensive Survey of Data Mining Techniques on Time Series Data for Rainfall Prediction  

Oleh:

Mishra, Neelam ; Soni, Hemant Kumar ; Sharma, Sanjiv ; Upadhyay, A.K.

Jenis: Article from Journal - ilmiah nasional - terakreditasi DIKTI
Dalam koleksi: Journal of ICT Research and Applications vol. 11 no. 2 (2017), page 168-184.
Topik: data mining; intelligent forecasting model; neural network; rainfall forecasting; rainfall and runoff patterns; statistical techniques; time series data mining; weather prediction.
Fulltext: 2604-18843-3-PB_Ros.pdf (265.76KB)

Isi artikel

Time series data available in huge amounts can be used in decisionmaking. Such time series data can be converted into information to be used for forecasting. Various techniques are available for prediction and forecasting on the basis of time series data. Presently, the use of data mining techniques for this purpose is increasing day by day. In the present study, a comprehensive survey of data mining approaches and statistical techniques for rainfall prediction on time series data was conducted. A detailed comparison of different relevant techniques was also conducted and some plausible solutions are suggested for efficient time series data mining techniques for future algorithms.

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