Predicting time-series for water demand in the big data environment using statistical methods, machine learning and the novel analog methodology dynamic time scan forecasting

Abstract

The specialized literature on water demand forecasting indicates that successful predicting models are based on soft computing approaches such as neural networks, fuzzy systems, evolutionary computing, support vector machines and hybrid models. However, soft computing models are extremely sensitive to sample size, with limitations for modeling extensive time-series. As an alternative, this work proposes the use of the dynamic time scan forecasting (DTSF) method to predict time-series for water demand in urban supply systems. Such a model scans a time-series looking for patterns similar to the values observed most recently. The values that precede the selected patterns are used to create the prediction using similarity functions. Compared to soft computing approaches, the DTSF method has very low computational complexity and is indicated for large time-series. Results presented here demonstrate that the proposed method provides similar or improved forecast values, compared to soft computing and statistical methods, but with lower computational cost. Thus, its use for online water demand forecasts is favored.