Another inconvenience appears when the flow meter malfunctions or when there are issues for transmitting the signal, common in chambers and places prone to floodings. connected with the PRV. As a result, the installation and commissioning process for the utilities is easier and also lowers the investment required. The use of statistical and mathematical techniques for the analysis of time series has been known since the 19 th century. A time series is a set of observations ordered or plotted against time and conclusions drawn from them. In the early 1970s, [PERSON] and [PERSON] (1976) [32] devised a statistical methodology for time series analysis, applied to different scientific branches, which were latter named as ARIMA models. The ARIMA model, in its simplest version, has only three components that give the model its name: AR for autoregressive, I for integrated and MA for moving averages. Each part is represented by a positive integer (p,d,q). AR, or autoregressive, models are used under the condition that past values condition or correlate with current values, and MA, or moving average, models take into account past residuals to correctly fit the model. The application of ARIMA models requires the time series to be stationary: mean and variance are stable over time. The SARIMA model is an extension of the ARIMA model that allows modeling non-stationary series. The ARIMA model has been employed in numerous research fields. For example, it has been used for modeling and forecasting water consumption by [PERSON] et al. [33], [PERSON] and [PERSON] [34], [PERSON] et al. [35], [PERSON] [36] and [PERSON] et al. [37]. In the medical research field, [PERSON] et al. [38] explored the demographic characteristics of acute hemorrhagic conjunctivitis using a SARIMA model. [PERSON] [39] modeled and forecast the inflation rate in developing countries, focusing on Turkey and [PERSON] et al. [40] on Nigeria. [PERSON] et al. [41] forecast international tourist arrivals in Zimbabwe. [PERSON] et al. [42] used the SARIMA model to forecast the price of red lentils in Canada, the largest producer in the world. [PERSON] et al. [43] used the model for time series from monthly rainfall and [PERSON] et al. [44] for electric vehicle charging demand. [PERSON] and [PERSON] [45] used SARIMA models for monthly, weekly and daily monsoon rainfall time series. [PERSON] [46] validated SARIMA models to forecast long-term runoff in the United States. [PERSON] and [PERSON] [47] applied ARIMA models to forecast gas consumption in Turkey to improve demand forecasting. This paper describes a time-based open-loop CPC model, with SARIMA applied to pressure management (PM) in WDN. The model relates the ratio of the system critical point pressure (_P3_) to the pressure at the outlet of the valve (_P2_) as a function of time (_P3_/_P2_), creating a predictive model which assures target pressure in the critical point of the WDN. The model has been developed and tested on a real location in the village of Noja in northern Spain. This village is characterized by great seasonal demand variation throughout the year. Currently, Noja water utility is applying CPC based on an open-loop control model, which relates headloss between the outlet of the valve and the critical point against the flow at the inlet of the WDN. This pressure-control strategy has been identified as the most beneficial by the water utility since it allows to keep pressure at the minimum level in the WDN, while assuring the right level of service to the customers, under the challenging seasonal demand pattern of the area. However, due to the criticality of the installation, especially during summer and bank holidays when the area is full of tourists, it would be desirable to have an alternative control scheme in case of malfunction of the flow meter to maintain target pressure in the critical point regardless of the flow measurement. This fact motivates this research and the elaboration of this paper.