## 1 Introduction  
In the recent centuries, climate change-related phenomena and the rapid increase in population have dramatically increased the exploitation of freshwater resources causing deterioration in groundwater quality and raising the probability of severe droughs. Therefore, the demand for better information on groundwater quantity and quality trends and how best to equitably manage them is growing worldwide [1; 2; 3]. However, these trends consist of an integrated response to several climatic, anthropogenic, and hydrogeological factors and their interactions, which makes prediction both for the short and long term a challenging task. Natural geological systems exhibit a large spatial variability of hydrogeological properties over a wide range of scales. Numerical modelling is often performed to gain an understanding of the hydraulic nature of the aquifer and to predict contaminant transport; these models require spatial distributions of hydraulic properties as inputs [4]. It is typically not feasible to describe the hydraulic properties over the entire flow domain at the resolution needed for accurate flow modelling using conventional hydraulic property measurement techniques alone [5; 6]. The ability to describe adequately these properties is strongly dependent on the availability and distribution of field measurements correlated with the hydrological heterogeneities [7]. The need for a comprehensive understanding of fluid transport behavior, such as flow pathways, flow velocity, and hydraulic conductivity in heterogeneous aquifers, has driven improvements of near-surface geophysical methods able to provide densely spatial and temporal knowledge about these subsurface properties [8; 9; 10; 11]. Among others, the direct current (DC) method is probably the most widely used near-surface geophysical techniques for these scopes because subsurface electrical resistivity properties are correlated to physical, chemical, and lithologic properties of subsoil (e.g., saturation and salinity of fluids within the pore space, porosity, and clay content). Furthermore, measurement techniques are highly scalable, allowing for investigations at depths to tens of centimeters to hundreds of meters [12; 13]. In the last two decades, the DC electrical resistivity method has evolved significantly through the introduction of cross-hole electrical resistivity tomography (CH-ERT) [14] and 3D or 4D monitoring by time-lapse surveys as well as permanent measurement systems [15; 16; 17; 18; 19]. Nowadays, CH-ERT, combined with time-lapse surveys, has become very common in environmental investigations to quantify the hydraulic parameters and to obtain information on the spatial variability of solute transport processes allowing for better deep resolution and anisotropy detection compared to surface measurements [20; 21]. This type of survey commonly produces large volumes of data in the form of time series, representing how resistivity distribution changes in the subsoil over time. These variations can be correlated to various possible causes, such as water injection/production, pollution diffusion, and so forth [22; 23; 24]. If a sufficiently large historical database is available, it is possible to make probabilistic predictions about future resistivity changes by applying statistical and predictive algorithms to time-lapse ERT data. So, the total knowledge acquired about past, current, and predicted fluid displacement, contaminant diffusion, and aquifer hydraulic properties can be used to define an optimal policy of aquifer management. In general, the main objective of time series analysis is to extract meaningful characteristics and statistical information from data organized in chronological order for different purposes, such as analysis of past behavior, prediction, and forecasting of future trends, curve fitting, interpolation and extrapolation, classification and clustering, segmentation and decomposition, frequency characterization, etc. [25].