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The main goal of machine learning (ML) is to study and improve mathematicalmodels which can be trained with data provided by the environment to infer thefuture and to make decisions without necessarily having complete knowledge ofall influencing elements. In this work, we describe how ML can be a powerfultool in studying climate modeling. Tree ring growth was used as animplementation in different aspects, for example, studying the history ofbuildings and environment. By growing and via the time, a new layer of wood tobeneath its bark by the tree. After years of growing, time series can beapplied via a sequence of tree ring widths. The purpose of this paper is to useML algorithms and Extreme Value Theory in order to analyse a set of tree ringwidths data from nine trees growing in Nottinghamshire. Initially, we start byexploring the data through a variety of descriptive statistical approaches.Transforming data is important at this stage to find out any problem inmodelling algorithm. We then use algorithm tuning and ensemble methods toimprove the k-nearest neighbors (KNN) algorithm. A comparison between thedeveloped method in this study ad other methods are applied. Also, extremevalue of the dataset will be more investigated. The results of the analysisstudy show that the ML algorithms in the Random Forest method would giveaccurate results in the analysis of tree ring widths data from nine treesgrowing in Nottinghamshire with the lowest Root Mean Square Error value. Also,we notice that as the assumed ARMA model parameters increased, the probabilityof selecting the true model also increased. In terms of the Extreme ValueTheory, the Weibull distribution would be a good choice to model tree ringdata.

Machine Learning Approach and Extreme Value Theory to Correlated Stochastic Time Series with Application to Tree Ring Data