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

Neelam Mishra (Department of Computer Science and Engineering, NRI College of Engineering and Management, Gwalior, Madhya Pradesh)
Hemant Kumar Soni (Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Madhya Pradesh, Gwalior)
Sanjiv Sharma (Department of Computer Science and Engineering, Madhav Institute of Technology and Science, Gwalior, Madhya Pradesh)
A.K. Upadhyay (Department of Computer Science and Engineering Amity School of Engineering and Technology, Amity University, Madhya Pradesh, Gwalior)

Journal of ICT Research and Applications
Vol. 11 No. 2 (2017)

Time series data available in huge amounts can be used in decision-making. 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.