Work place: Department of Polymer and Process Engineering, Indian Institute of Technology, Roorkee, 247667, India
Research Interests: Data Structures and Algorithms, Data Mining, Computer systems and computational processes
Tripti Mahara received the B. E degree in Computer Science & Engineering from Sardar Patel University in 1999, M.Tech and Ph.D. from Industrial and Management Engineering, I.I.T Kanpur in 2004 and 2009, respectively. Currently, she is Assistant Professor with the Department of Polymer and Process Engineering IIT Roorkee, India. She has published over 18 referred journals and conference papers. She works in the area of Recommender System and Predictive Analytics and Data Mining.
DOI: https://doi.org/10.5815/ijigsp.2018.05.04, Pub. Date: 8 May 2018
This paper seeks to evaluate the appropriateness of various univariate forecasting techniques for providing accurate and statistically significant forecasts for manufacturing industries using natural gas. The term "univariate time series" refers to a time series that consists of single observation recorded sequentially over an equal time interval. A forecasting technique to predict natural gas requirement is an important aspect of an organization that uses natural gas in form of input fuel as it will help to predict future consumption of organization.We report the results from the seven most competitive techniques. Special consideration is given to the ability of these techniques to provide forecasts which outperforms the Naive method. Naïve method, Drift method, Simple Exponential Smoothing (SES), Holt method, ETS(Error, trend, seasonal) method, ARIMA, and Neural Network (NN) have been studied and compared.Forecasting accuracy measures used for performance checking are MSE, RMSE, and MAPE. Comparison of forecasting performance shows that ARIMA model gives a better performance.[...] Read more.
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