On the use of ARIMA models for short-term water tank levels forecasting

Abstract

In this paper a statistical study on the time series of water levels measured, during 2014, in the water tank of Cesine, Avellino (Italy), is presented. In particular, the autoregressive integrated moving average (ARIMA) forecasting methodology is applied to model and forecast the daily water levels. This technique combines the autoregression and the moving average approaches, with the possibility to differentiate the data, to make the series stationary. In order to better describe the trend, over time, of the water levels in the reservoir, three ARIMA models are calibrated, validated and compared: ARIMA (2,0,2), ARIMA (3,1,3), ARIMA (6,1,6). After a preliminary statistical characterization of the series, the models' parameters are calibrated on the data related to the first 11 months of 2014, in order to keep the last month of data for validating the results. For each model, a graphical comparison with the observed data is presented, together with the calculation of the summary statistics of the residuals and of some error metrics. The results are discussed and some further possible applications are highlighted in the conclusions.