Open Issues and Challenges on Time Series Forecasting for Water Consumption

In this paper we study the problem of water consumption forecasting, an instance of the general time series forecasting problem, that has not been explored adequately. We base our analysis on two types of data: aggregate and individual consumptions measured by Smart Water Meters. We evaluate a series of state of the art forecasting algorithms and showcase that these models are not suitable for every instance of the forecasting problem: while they work effectively on aggregated data that contain strong seasonal patterns, their performance drops dramatically on individual user consumption time series, where such patterns are weaker. To this end, we identify open issues and challenges on the problem and, also, demonstrate that a simpler model we propose can outperform several of the aforementioned algorithms, although still needing significant improvements.