## 1 Introduction
Ground-level ozone concentrations are of significant concern due to their toxic agents, which can adversely affect the respiratory systems of people who inhale high ozone concentrations for extended periods. These adverse health effects can lead to decreased lung function, chest pain while breathing, coughing, throat infections, congestion, and worsening symptoms of asthma. Time series record the observations made in a particular place and are associated with the evolution over time of a particular variable; observed behavior cannot be replicated with repeated experiments, and observations are often time-dependent. This information has allowed the development of traditional deterministic modeling and statistical models. Although chemical transport models have generally been applied to differentiate emission sources and meteorological variables to explain short- or long-term ozone fluctuations, temporal analysis can show spatial and seasonal changes in the distribution of ozone concentrations [6]. At the same time, statistical models are generated by relational analysis between factors influencing pollutants, producing powerful statistical prediction equations [7]. However, when you want to study the behavior in the spatiotemporal distribution of a pollutant, the problem lies in the variability of pollutant concentrations, which are strongly influenced by the fluctuations of the emitting sources and the meteorological state--hourly, daily, seasonal, and annual. Thus, the impact exerted by trends in the behavior of air pollutants may be beneficial to optimize the performance of modeling [7]. Currently, statistical modeling is evolving, including the management of time series that deserves to be compared with traditional models, mainly multiple linear regression. However, it is still necessary to continue exploring new studies to improve the prediction of reliable models, the reduction of noise through filters, and the organization of the numerical information of contaminants [7]. Decomposition is a methodology applied to analyze time series air pollutant data; the decomposition in ensemble empirical mode is counted to process these non-stationary and nonlinear signals and allows one to gradually separate the different fluctuation components [8]. Generally, the numerical data of the ozone time series have various types of patterns, so it is essential to break the database into several components or sub-classes in such a way that each one is a unique pattern of the data. Furthermore, the time series for an air pollutant is considered to be additive and may comprise elements over time [6]. Interrupted time series designs are a powerful tool for comparing the variation of levels and the trend of results [9]. According to Din [6], the ozone concentration at time \(t\) is given by the sum of each component (from decomposition). One component is given by the \"trend\" of time in the time series and is relevant to the persistent decrease or increase in ozone concentration driven via emission sources or meteorological variables. For its part, the second component, \"seasonality\", describes the fluctuations of the periodic seasons (decomposed), and the third, fixed by a short-term component, shows the \"rest\" of the random data once the first two components have been separated. In addition, other combined decompositionmethods or structures have been proposed in series and time convolution and long-term short-memory bidirectional networks [10]. Other models use a non-parametric Theil-Sen estimator as a robust Kendall [11] line-fit method or locally estimated scatterplot models for smoothing to filter the data obtained and subsequently decompose the time series models into trend, seasonal, and residual components of data and then recombine them appropriately [12]. After the decomposition of the time series into components or subseries, the data can be used in standardized time series modeling as linear, nonlinear autoregressive, or autoregressive moving averages.