Selected Polynomial Identification Techniques to Evaluate Maritime Transport Trends Around Covid-19

Abstract

The Covid-19 pandemic has drastically affected the transport sector, because of the restrictions introduced to limit the spread of the threat. They concerned primarily passenger traffic, but trade in goods also faced completely new challenges, related to increased consumption and the dynamic development of e-commerce on the one hand and restrictions related to the pandemic and sealing borders on the other. One of the most susceptible to fluctuations in international trade is the maritime economy, which has been analysed in this article. It was checked how the global threat affected sea traffic in terms of gross weight of goods handled in main ports. The aim of the study was to characterize the impact of the pandemic on sea transport depending on the type of ship and to evaluate the current state of sea transport in the context of the level shaped by forecasts based on observations from before the coronavirus pandemic. The authors' assumption was to check whether the rail transport market has already reached the level it could reach in the absence of the virus threat. The use of a polynomial function was proposed for the study. Time series containing observations up to the outbreak of the pandemic and forecasts based on them, as well as time series containing additional observations from the pandemic period were analysed. The study results obtained allowed to conclude how the global crisis caused by the Covid-19 pandemic affected the cargo traffic in the sea transport, expressed by the mass of goods transshipped in major ports, depending on the individual types of ships.

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“…If non-stationarity is detected using the ADF test in the x j t−m≤j≤t time series, we adjust the trend in this series with a polynomial of the appropriate degree [17,28,[31][32][33].…”

“…If the stationarity postulate is not met, then based on the implementation of x j max(0,t−m)≤j≤t the trend in the series should be identified [28]. To verify the trend in time series, we use the tests [29][30][31][32][33] and model the trend using a polynomial [18,19] of a certain degree (if the degree is equal to one, there is a simple linear trend).…”