Oil and gas as the non-renewable resources are considered very valuable for the countries with petroleum economics. These resources are not only diffused equally around the world, but also they are common in some places which their neighbors often come into conflicts. Consequently, it is vital for those countries to manage their resource utilization. Lately, game theory was applied in conflict resolution of common resources, such as water, which is a proof of its efficacy and capability. This paper models the conflicts between Iran and its neighbors namely Qatar and Iraq between their oil and gas common resources using game theory approach. In other words, the future of these countries will be introduced and analyzed by some well-known 2 9 2 games to achieve a better perspective of their conflicts. Because of information inadequacy of the players, in addition to Nash Stability, various solution concepts are used based on the foresight, disimprovements, and knowledge of preferences. The results of mathematical models show how the countries could take a reasonable strategy to exploit their common resources.
The COVID-19 pandemic has disrupted the economy and businesses and impacted all facets of people’s lives. It is critical to forecast the number of infected cases to make accurate decisions on the necessary measures to control the outbreak. While deep learning models have proved to be effective in this context, time series augmentation can improve their performance. In this paper, we use time series augmentation techniques to create new time series that take into account the characteristics of the original series, which we then use to generate enough samples to fit deep learning models properly. The proposed method is applied in the context of COVID-19 time series forecasting using three deep learning techniques, (1) the long short-term memory, (2) gated recurrent units, and (3) convolutional neural network. In terms of symmetric mean absolute percentage error and root mean square error measures, the proposed method significantly improves the performance of long short-term memory and convolutional neural networks. Also, the improvement is average for the gated recurrent units. Finally, we present a summary of the top augmentation model as well as a visual representation of the actual and forecasted data for each country.
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