Author:
Shaik Mohammed Ali,Verma Dhanraj
Abstract
Abstract
In the present era most of the researchers has done extensive research in forecasting the time series data using distinct “linear, nonlinear and hybrid linear models” that combines models like “Auto Regressive Integrated Moving Average (ARIMA)” using “Artificial Neural Networks (ANN)” by combining the “Time Series Forecasting (TSF)”. In this paper we proposed a new algorithm which will smooth the data and evaluate the error rate to obtain time series predictions by considering Telangana state rainfall dataset. Our experimental illustrations propose that the results attained are statistically promising with the datasets that are used for generating the acceptable prediction result set.
Reference51 articles.
1. A moving-average filter based hybrid ARIMA-ANN model for forecasting time series data;Babu;Appl. Soft Comput,2014
2. Power quality time series data mining using S-transform and fuzzy expert system;Behera;Appl. Soft Comput,2010
3. Time Series Analysis, Forecasting and Control;Box,1990
4. A detailed literature review on wind forecasting;Chandra,2013
5. Statistical comparisons of classifiers over multiple datasets;Demsar;J. Mach. Learn. Res,2006
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