• RUPA. A. FADNAVIS

Time series data mining plays a major role in organizations decision making. Time series data mining involves extraction of all meaningful knowledge and patterns from data collected over a period of time. Various data mining techniques like classification, clustering, segmentation, anomaly detection, prediction are applied to different type of time series data to find useful and interesting patterns from it. This paper focuses on one such important mining task performed on time series data - prediction. Various statistical methods used for time series prediction includes regression, exponential smoothing, moving averages, autoregressive and moving average models, etc. Many times complexity underlying these methods precludes its use by those less acquainted with them. So it is possible to use heuristic method which gives approximated, but satisfactory solution for such class of problems. This paper proposes heuristic approach for time series data mining task-prediction. The approach used is based on evolutionary computation technique- genetic algorithm. Prediction results are evaluated on the basis of R2 and Root mean square error value (RMSE).

Time series, Time series data mining, prediction, genetic algorithm

"APPLICATION OF GENETIC ALGORITHM TO TIME SERIES DATA MINING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.207-211, July-2018, Available :http://www.jetir.org/papers/JETIR1807927.pdf

"APPLICATION OF GENETIC ALGORITHM TO TIME SERIES DATA MINING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp207-211, July-2018, Available at : http://www.jetir.org/papers/JETIR1807927.pdf