Dynamic Nature of Explanatory Variables in Water Demand Forecasting

Researchers have used a wide range of explanatory variables in their water demand forecasting models. In reality, nature of the data used in these modelling techniques is not similar to each other. In fact, not enough attention has been paid to periodic or chaotic nature of the time series deployed in water demand forecasting techniques. The purpose of this study was to quantify chaos in weather information variables used in demand forecasting models. Using correlation dimension method to check the evidence of deterministic chaos, this research proved these explanatory variables can exhibit high dimensional chaos in stochastic systems.