## 1 Introduction
Linear models such as autoregressive are difficult to handle with nonlinear and time-varying data [13]. However, the application of combined auto-correlation function (ACF) and partial autocorrelation function (PACF) graphs overcomes the limitations of simple techniques by showing the correlation between the time series and the lags after excluding the contributions from previous lags [14]. [PERSON] et al. [15; 16] applied a nonlinear autoregressive model relating a past value and smoothed functions of the original values of the time series. An autoregressive moving average was also applied to take into account the errors that make up the model, as well as linear models of combination for all lag observations and the lag error term. On the other hand, machine learning models have also been used to forecast ozone levels.For instance, the researchers in [17] proposed a deep learning model for the prediction of ozone levels in Aarhus using a grid search technique and implemented it as an accuracy tool for forecasting ozone levels in smart cities. The ozone concentration in India is predicted [18] using eight machine learning models, including XGboost, random forest, k-nearest neighbor, support vector regression, decision tree, Adaboost, linear regression, and bidirectional long-short-term memory, which achieved the predictive capabilities with a R\({}^{2}\) of 0.75 in winter. The researchers further divided the predicted capabilities in terms of season, and the winter season was found to be more predictable with 97.3%, post-monsoon 92.8%, monsoon 90.3%, and summer 88.9%. The authors in [19; 20; 21] applied time series, hybrid decomposition, machine learning, and deep learning models for forecasting ozone concentration in Tehran, Iran, in 11 municipal districts of Nanjing, China, and 8 out of 35 stations in Turkey. Peru is a country located in South America in the Southeast Pacific Region, and its capital, Lima, is no stranger to ozone air pollution. Lima has become a megacity with more than 10 million inhabitants and severe air pollution problems. [PERSON] et al. [22] evaluated the impact of meteorological variables on the ozone concentration and other pollutants present in the air through linear correlations made for data obtained between 2015 and 2018 at eight sampling stations in metropolitan Lima and reported that this pollutant increased with solar irradiation around 10:00 and 16:00 h, especially in spring, possibly caused by the interaction of primary NOx and hydrocarbon emissions from vehicle engines. [PERSON] et al. [23] instead studied the behavior of ozone in winter using machine learning algorithms in four stations in the city of Lima and found the highest critical levels (165.80 ug/m\({}^{3}\)) in the Ate district (ATE). However, we observed, in general, a drop in values in the cold season (O\({}_{3}\)! 100 ug/m\({}^{3}\)), similar to another study [24]. At the same time, there is a need to comprehensively analyze the time series of the most polluted districts to optimize the prediction of ozone concentration. In this context, this research aims to propose an improved tool to forecast tropospheric ozone concentration using hybrid combinations of time series models in four districts of the megacity of Metropolitan Lima in a very precise way, through an innovative methodology based on the decomposition of a time series of data and the combination of traditional methods to achieve efficient predictions. The following are the contributions of this research:  
* We improve the efficiency and accuracy of one-hour-ahead ozone concentration forecasting using a proposed hybrid combination of time series models based on the seasonal trend decomposition technique and various standard time series models. * We apply the seasonal trend decomposition method of the ozone concentration database in four districts--ATE, Campo de Marte (CDM), San Borja (SB), and Santa Anita (STA)--with severe episodes of ozone contamination between 2017 and 2019.