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Title: Time series analysis and prediction of electricity consumption of health care institution using ARIMA model
Authors: Kaur H
Ahuja S.
Keywords: Time series
Akaike Information Criterion
Schwarz Bayesian Criterion
Autoregressive integrated moving average model
SAS University Edition.
Issue Date: 2017
Publisher: Springer Verlag
Abstract: The purpose of this research is to find a best fitting model to predict the electricity consumption in a health care institution and to find the most suitable forecasting period in terms of monthly, bimonthly, or quarterly time series. The time series data used in this study has been collected from a health care institution Apollo Hospital, Ludhiana for the time period of April 2005 to February 2016. The analysis of the time series data and prediction of electricity consumption have been performed using ARIMA (Autoregressive Integrated Moving Average) model. The most suitable candidate model for the three time series is selected by considering the lowest value of two relative quality measures i.e. AIC (Akaike Information Criterion) and SBC (Schwarz Bayesian Criterion). The appropriate forecasting period is selected by considering the lowest value of RMSE (Root Mean Square Error) and MPE (Mean Percentage Error). After building the final model a two-year prediction of electricity consumption of the health care institution is performed.
URI: 10.1007/978-981-10-3325-4_35
Appears in Collections:Conferences

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