This paper presents a novel approach, named the SVR (support vector regression) based SVRLP (support vector regression local predictor) with FNF-SVRLP (false neighbours filtered-support vector regression local predictor), to predict short-term natural gas demand. This method integrates the SVR algorithm with the reconstruction properties of a time series, and optimises the original local predictor by removing false neighbours. A unified model, named the SM ("Standard Model"), is presented to process the entire dataset. To further improve the predicted accuracy, an AM ("Advanced Model") is proposed, and is based on specific customer behaviours during different days of the week. The AM contains seven individual models for the seven days of the week. The FNF-SVRLP based AM has been used to predict natural gas demand for the National Grid of the United Kingdom (UK). This model outperforms the SVRLP, the ARMA (autoregressive moving average) and the ANN (artificial neural network) methods when applied to real-world data obtained from National Grid and has been successfully applied to daily gas operations for National Grid. (C) 2014 Elsevier Ltd. All rights reserved.