Work place: Department of Polymer and Process Engineering, Indian Institute of Technology, Roorkee, 247667, India.
Research Interests: Data Mining, Computer Networks, Neural Networks, Computational Learning Theory
Iram Naim was born on May 26, 1984. She received the B. Tech. degree from M. J. P. Rohilkhand University, Bareilly, inyear 2007. She did M. Tech from Aligarh Muslim University, Aligarh in the year 2011. Currently, she is a Ph. D. Scholar in Indian Institute of Technology (IIT), Roorkee. In 2008, she joined the M.J.P. RohilkhandUniversity, Bareilly as an Assistant Professor. Herpublished articles lists is as follow: User feedback based metasearching using neural network(International Journal of Machine Learning and Cybernetics (2015) 6(2)), Metasearching using modified rough set based rank aggregation (International Conference on Multimedia, Signal Processing and Communication Technologies, IMPACT 2011 (2011)). Her recently presented articles include Effect of Energy Resource Consumption Forecasting on Procurement Strategy: (In 11th ISDSI International Conference, Indian Institute of Management (IIM) Tiruchirappalli, Tamil Nadu, India.)Forecasting of Energy for Manufacturers using Arima and Neural network (5th international conference on business analytics and intelligence, Institute of Management (IIM) Bangalore)etc.Her research interests include Data Mining, Machine Learning, Neural Network, and Time series forecasting methods.
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.
Subscribe to receive issue release notifications and newsletters from MECS Press journals