A Conceptual Forecasting Analysis of International Trade Aspects Based on Globalization Impact Issues

Global GDP is really important for trade, since the larger the global economy, the more goods and services available for trade. Global GDP grew by around two-thirds in real terms between 2000 and 2020 – or 2.6% per year on average.2020 saw some of the largest trade reductions and output volumes for both industrial production and goods trade since WWII. The year 2020 was marked by some of the largest reductions in trade and output volumes since WWII. The declines in both world industrial production and goods trade in the first half of 2020 were of similar depth to those at the trough of the Global Financial Crisis (GFC). In addition, trade and production impacts across specific goods, services and trade partners were highly varied. Initial pandemic-era expectations for a double-digit decline in world merchandise trade in 2020 did not materialise. Global trade turned out to recover from the shock at an extraordinarily fast pace from around mid-2020.

PERBANDINGAN METODE SEASONAL ARIMA DAN EXTREME LEARNING MACHINE PADA PERAMALAN JUMLAH WISATAWAN MANCANEGARA KE BALI

Bali Island of the Gods is one of the wealth of very popular tourist destinations and has the highest number of foreign tourists in Indonesia. It is very necessary to do more in-depth learning related to the projections or forecasting of foreign tourist visits to Bali at a certain period of time. Forecasting analysis used is to compare two methods, namely the Seasonal ARIMA method (SARIMA) and Extreme Learning Machine (ELM). The SARIMA method is a statistical method commonly used in forecasting time series data that contains seasonality and has good accuracy. While the ELM method is a new learning method of artificial neural networks that has fast learning speed and good accuracy. The results obtained indicate that the Seasonal ARIMA method is a better method used to predict the number of tourists to Bali in this case, because it has a smaller forecasting MAPE value of 4.97%. While the ELM method has a forecasting MAPE value of 7.62%.

PERAMALAN INFLASI DI INDONESIA MENGGUNAKAN METODE AUTOREGRESSIVE MOVING AVERAGE (ARMA)

A good inflation rate for a country is an inflation rate that has a low and stable value so that able to realize fast and controlled economic growth. Forecasting can be one of the steps that can provide an overview of the value of inflation in Indonesia for the government or related agencies to formulate and maintain inflation stability in Indonesia. In this study, a forecasting analysis was carried out to determine the prediction of inflation in Indonesia in 2021 using the Autoregressive Moving Average (ARMA) method. From the results of the research that has been done, the best model to predict this case is using the ARMA model (3,0,0) because it produces the smallest AIC value of 0.2373 and the smallest RMSE of 7.81. From this model, the results of forecasting inflation rates for the months of May to December 2021 are also obtained with a range of 0.1% to 0.3%. The graphic pattern of the predicted results follows the actual data line pattern, which means that this model is good to use. Abstrak Tingkat inflasi yang baik bagi suatu negara adalah tingkat inflasi yang memiliki nilai yang rendah dan stabil, sehinga mampu mewujudkan pertumbuhan ekonomi yang cepat dan terkendali. Peramalan dapat menjadi salah satu langkah yang dapat memberikan gambaran nilai inflasi di Indonesia bagi pemerintah atau badan yang terkait untuk menyusun dan mempertahankan kestabilan inflasi di Indonesia. Dalam penelitian ini, dilakukan analisis peramalan untuk mengetahui prediksi angka inflasi di Indonesia tahun 2021 menggunakan metode Autoregresif Moving Average (ARMA). Dari hasil penelitian yang telah dilakukan, model terbaik untuk meramalkan kasus ini yaitu menggunakan model ARMA (3,0,0) karena menghasilkan nilai AIC paling kecil yaitu 0.2373 dan RMSE terkecil sebesar 7.81. Dari model tersebut juga didapatkan hasil peramalan angka inflasi untuk bulan Mei hingga Desember 2021 dengan kisaran 0.1% hingga 0.3%. Pola grafik dari hasil prediksi mengikuti pola garis data aktual yang berarti bahwa model ini baik untuk digunakan.

THE IMPACT OF DIGITALIZATION AND INTELLIGENTIZATION ON AIR TRANSPORTATION SYSTEM

This study aims to provide insights into the impact levels of digitalization and intelligentization on air transport system (ATS) in Australia, China, the US, and India. Air transport system is one of the most efficient transport systems which contains three elements: air traffic control, airport, and airlines. In modern society, the importance of digitalization and intelligentization in ATS is attached to by publics. In this study, firstly, comparative analysis is used to analyze the different states of digitalization and intelligentization level and air transport system in sample countries. Then, correlation analysis is used to study the correlation of the different impact factors with the ATS in different countries. The third one is regression analysis, it is used to analyze the relationship between ATS and the development of digitalization and intelligentization in four sample countries. At last, forecasting analysis is used to predict the future trend of digitalization and intelligentization’s impact on ATS in the sample countries in the next few years. Then, the most significant impact factors for ATS will be obtained. Also, the future development trends of ATS under digitalization and intelligentization’s impact could be forecasted by using econometric models.

Urban Agglomeration As a Factor of Sustainable Development of Satellite Cities (On the Example of Rostov Region)

The article discusses the features of the development of the largest Rostov agglomeration in the south of the country in order to form a single territorial, economic and social space. The authors analyze the conditions for agglomeration development and identify the necessary steps for the sustainable development of the cities that are part of the agglomeration, which would correspond to the interests of the population, business and local authorities. Within the framework of the article, the agglomeration effect on the territory of the agglomeration is revealed and the most urgent issues that have to be faced in the process of its formation are identified. To do this, the following methods were used in the article: systematization, forecasting, analysis, mathematical modeling and evaluation of the degree of agglomeration effect of various urban agglomerations with their comparison. The article provides a brief analysis of the largest cities that are part of the agglomeration, with an assessment of their specialization and influence on the development of the surrounding area. The main variables are statistical data on the population, the investment attractiveness of the territory and the volume of goods produced by the company. Special attention is paid to satellite cities, which are most influenced by the core city. The research conducted in the article allowed us to show that Bataysk and Aksay (the project “Big Rostov”) developing in a single cluster with Rostov are able to achieve many competitive advantages, both for the entire territory and for each specific municipality. Based on the interaction of these cities, a synergistic effect is shown, which is characterized by an active pendulum migration, development of communications and construction, concentration of production and specialization of the economy of the municipalities that are part of the agglomeration.

Weather Forecasting Based on Data-Driven and Physics Informed Reservoir Computing Models

In response to the growing demand for the global energy supply chain, wind power has become an important research subject among studies in the advancement of renewable energy sources. The major concern is the stochastic volatility of weather conditions that hinder the development of wind power forecasting approaches. To address this issue, the current study proposes a weather prediction method divided into two models for wind speed and atmospheric system forecasting. First, the data-based model incorporated with wavelet transform and recurrent neural networks is employed to predict the wind speed. Second, the physics-informed echo state network was used to learn the chaotic behaviour of the atmospheric system. The findings were validated with a case study conducted on wind speed data from Turkmenistan. The results suggest the out-performance of physics-informed model for accurate and reliable forecasting analysis, which indicates the potential for implementation in wind energy analysis.

A Momentary Lapse of Reason? Assessing the Pre- and Post-Brexit Preferences of UK Citizens

We analyze the persistence of the major determinants of distrust toward the European Union (EU) and pro-Brexit voting attitudes of citizens of the United Kingdom. Looking both backward and forward, our analysis confirms that Euroscepticism is deep-rooted, although the data reveal differences across different parts of the country. We observe no major transformation in the favor of the EU over the last two decades or in the post-referendum period. We also carry out an age-period-cohort analysis by interacting age and cohort effects with time to analyze the evolution of individual preferences. We find that an aging population will promote Eurosceptic assertiveness. We then implement a demographic forecasting analysis to predict whether the level of distrust of older cohorts can be offset by differing attitudes among younger cohorts in the future. On the contrary, we find that demographic trends will tend to strengthen distrust in the EU.