###### Abstract
The driving idea of Temporal J48 is no different from the classical regression tree extraction, that is Temporal J48 is a greedy, variance based extraction algorithm (it is, in fact, adapted from WEKA's implementation of J48 [20]). As a consequence, at each learning step, a local optimum is searched to perform a split of the data set, leading, in general, to not necessarily optimal trees. This problem exists in the non-temporal case, and not only in decision/regression trees. In a typical situation, greedy, locally optimal algorithms can be used in the context of feature selection, which is a meta-strategy that explores different selections of the independent variables and how such a selection influences the performances of the model. With Temporal J48, we can generalize such a concept to language and feature selection, that is the process of selecting the best features and the best interval relations for temporal regression. As it turns out, the techniques for feature selection can be applied to solve the feature and language selection problem. In this paper, we consider a data set with traffic volume values, meteorological values, and pollution values measured at a specific, highly trafficked street crossing in Wroclaw (Poland), from 2015 to 2017. Namely, we consider the problem of modelling the concentration of NO\({}_{2}\) (nitrogen oxide) in the air and define it as a temporal regression problem; by applying Temporal J48 to this problem, we approach and solve, more in general, a feature and language selection problem for symbolic temporal regression. To establish the reliability of our approach, we set an experiment with different subsets of the original data set, and we compare the results of temporal symbolic regression with those that can be obtained with other symbolic regression algorithms, such as (lagged or non-lagged versions of) regression trees and linear regressors, under the same conditions. As we find out, temporal symbolic regression not only returns interpretable models that enable the user to know why a certain prediction has been performed, but, at least in this case, the extracted models present statistically better and more reliable results. In summary, we aim at solving the problem of air quality modelling by defining it as a temporal regression problem, and we benchmark our proposed methodology based on temporal decision trees against methods that are present in the literature that may or may not consider the temporal component in an explicit way; the symbolic nature of the proposal allows naturally interpreting the underlying temporal theory that resembles the data by means of [PERSON] and [PERSON]'s logic. In this way, we hope to amend some of the well-known problems of prediction methods, including the post-hoc interpretability of the results. The paper is organized as follows. In Section 2, we highlight the needed background on the function and symbolic temporal regression problem, along with the feature selection process for regression tasks. In Section 3, we propose to solve the symbolic temporal regression problem by means of temporal decision trees. In Section 4, we formalize the feature and language selection learning process by means of multi-objective evolutionary optimization algorithms. In Section 5, we present the data used in our experiments and the experimental settings. The experiments are discussed in Section 6, before concluding.