The aim of this paper is the generation of a time-series based statistical data-driven procedure in order to track an outbreak. At first are used univariate time series models in order to predict the evolution of the reported cases. Moreover, are considered combinations of the models in order to provide more accurate and robust results. Additionally, statistical probability distributions are considered in order to generate future scenarios. Final step is the build and use of an epidemiological model (tSIR) and the calculation of an epidemiological ratio (R 0 ) for estimating the termination of the outbreak. The time series models include Exponential Smoothing and ARIMA approaches from the classical models, also Feed-Forward Artificial Neural Networks and Multivariate Adaptive Regression Splines from the machine learning toolbox. Combinations include simple mean, Newbolt-Granger and Bates-Granger approaches. Finally, the tSIR model and the R 0 ratio are used for estimating the spread and the reversion of the pandhemic. The suggested procedure is used to track the COVID-19 epidemic in Greece. This epidemic has appeared in China in December 2019 and has been widespread since then to all over the world. Greece is the center of this empirical study as is considered an early successful paradigm of resistance against the virus.

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This paper forecasts the daily Baltic Dry Index (BDI) using time series and machine learning methods. Significant business cycles and freight rate volatility present in the ocean‐going shipping industry make the ability to forecast freight rates and cycles extremely important for business decisions. Data‐driven model selection based on data characteristics is performed through ARIMA, fractional ARIMA (FARIMA), and ARIMA and FARIMA models with GARCH and EGARCH errors. The corresponding machine learning techniques utilized are feed‐forward fully connected artificial neural networks (ANNs), support vector regression (SVR), and multivariate adaptive regression splines (MARS). Among others, FARIMA and MARS models are used for the first time in forecasting the BDI. Diebold–Mariano tests reveal that time series and machine learning approaches have comparable performance. However, combinations of forecasts of the selected models lead to better forecasting accuracy overall. Bai and Perron tests are utilized to check the robustness of the results over different cycles through the detection of breakpoints in the series.