Forecasting household natural gas consumption with ARIMA model: A case study of removing cycle

Abstract

Forecasting natural gas consumption in Turkey is very important at energy sector. For this purpose kindly prediction methods are used. In this study autoregressive integrated moving average (ARIMA) method is used and main idea in this study is removing cycling component in time series. For removing cycling, time series divided monthly data and merged co-exhibiting behavior months. Same months and different years data is merged and called as “Model” and 6 Models are prepared. Last model; Model 7 is a general model that includes all consumption data. ARIMA models are applied and mean absolute percent errors (MAPE) are found. Selected minimum MAPE and values of (p, d, q) predictions for Models. For 2012, predictive values of models and Model 7 are compared with actual consumptions. Model that removed cycling (Merged Model) 2.2% better than Model 7.