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One of the main objectives of the time series analysis is forecasting, so both Machine Learning methods and statistical methods have been proposed in the literature. In this study, we compare the forecasting performance of some of these approaches. In addition to traditional forecasting methods, which are the Naive and Seasonal Naive Methods, S/ARIMA, Exponential Smoothing, TBATS, Bayesian Exponential Smoothing Models with Trend Modifications and STL Decomposition, the forecasts are also obtained using seven different machine learning methods, which are Random Forest, Support Vector Regression, XGBoosting, BNN, RNN, LSTM, and FFNN, and the hybridization of both statistical time series and machine learning methods. The data set is selected proportionally from various time domains in M4 Competition data set. Thereby, we aim to create a forecasting guide by considering different preprocessing approaches, methods, and data sets having various time domains. After the experiment, the performance and impact of all methods are discussed. Therefore, most of the best models are mainly selected from machine learning methods for forecasting. Moreover, the forecasting performance of the model is affected by both the time frequency and forecast horizon. Lastly, the study suggests that the hybrid approach is not always the best model for forecasting. Hence, this study provides guidelines to understand which method will perform better at different time series frequencies. [ABSTRACT FROM AUTHOR]

Why R? Turkey 2022 was a non-profit conference that aimed to bring Turkish R users together and encourage them to attend the R conferences. The targeted audience of the conference consisted of, data scientists, data analysts, and all R users from academia and industry. The three-day conference, which consisted of several events such as workshops, regular talks, lightning talks, short tutorials, and panels, was free of charge and fully online. This article describes the challenges and benefits, as well as providing an overview of the conference's content and participants' profile. [ABSTRACT FROM AUTHOR]

The Why R? Turkey 2021 as a three-day online conference was organized to bring together researchers and professionals from Turkey on April 16-17-18, 2021. We hereby aimed to promote the R community in Turkey by bringing R users with different backgrounds such as genetics, sociology, finance, economy, bio-statistics. There were 8 thematic sessions and 18 invited speakers. In this article, it is aimed to describe the preparation phase, technical details, and the impact of the conference on audience. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 virus pandemic (COVID-19) has caused 2.25 million deaths worldwide by February 3, 2021 (JHU, 2021) and still causing severe health and economic disruptions with increasing rates. This study investigates the impact of lockdown measures on ambient air pollution and its association with human mobility in 81 cities of Turkey. We conducted a countrywide analysis using PM 10 and SO 2 measurement data by the Turkish Ministry of Environment and Urbanization and mobility data derived from cellular device movement by Google. We observed the most significant change in April 2020. PM 10 and SO 2 concentrations were lower in 67% and 59% of the cities, respectively in April 2020 compared to the previous five years (2015-2019). The correlation results show that Restaurant/Café, Transit, and Workplaces mobility is significantly correlated with PM 10 and SO 2 concentration levels in Turkey. This study is the first step of a long-term investigation to understand the air quality impacts on population susceptibility to COVID-19., (Copyright © 2021 Elsevier Inc. All rights reserved.)