scholarly journals Evaluation of the Accuracy of Machine Learning Predictions of the Czech Republic’s Exports to the China

2021 ◽  
Vol 14 (2) ◽  
pp. 76
Author(s):  
Petr Suler ◽  
Zuzana Rowland ◽  
Tomas Krulicky
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Future Development ◽  
Explanatory Power ◽  
Republic Of China ◽  
China Trade ◽  
The Future ◽  
The Usa ◽  
Research Questions ◽  
Trade War

The objective of this contribution is to predict the development of the Czech Republic’s (CR) exports to the PRC (People’s Republic of China) using ANN (artificial neural networks). To meet the objective, two research questions are formulated. The questions focus on whether growth in the CR’s exports to the PRC can be expected and whether MLP (Multi-Layer Perceptron) networks are applicable for predicting the future development of the CR’s exports to the PRC. On the basis of previously obtained historical data, ANN with the best explanatory power are generated. For the purpose specified, three experiments are carried out, the results of which are described in detail. For the first, second and third experiments, ANN for predicting the development of exports are generated on the basis of a time series with a 1-month, 5-month and 10-month time delay, respectively. The generated ANN are the MLP and regression time series neural networks. The MLP turn out to be the most efficient in predicting the future development of the CR’s exports to the PRC. They are also able to predict possible extremes. It is also determined that the USA–China trade war has significantly affected the CR’s exports to the PRC.

scholarly journals Machine learning forecasting of CR and PRC balance of trade

2020 ◽  
Vol 73 ◽  
pp. 01004
Author(s):  
Tomàš Brabenec ◽  
Petr Šuleř
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Series ◽  
Data Set ◽  
Republic Of China ◽  
The People ◽  
The Future ◽  
Balance Of Trade ◽  
Political Economic ◽  
Social Importance

International trade is an important factor of economic growth. While foreign trade has existed throughout the history, its political, economic and social importance has grown significantly in the last centuries. The objective of the contribution is to use machine learning forecasting for predicting the balance of trade of the Czech Republic (CR) and the People´s Republic of China (PRC) through analysing and machine learning forecasting of the CR import from the PRC and the CR export to the PRC. The data set includes monthly trade balance intervals from January 2000 to June 2019. The contribution investigates and subsequently smooths two time series: the CR import from the PRC; the CR export to the PRC. The balance of trade of both countries in the entire monitored period is negative from the perspective of the CR. A total of 10,000 neural networks are generated. 5 neural structures with the best characteristics are retained. Neural networks are able to capture both the trend of the entire time series and its seasonal fluctuations, but it is necessary to work with time series lag. The CR import from the PRC is growing and it is expected to grow in the future. The CR export to the PRC is growing and it is expected to grow in the future, but its increase in absolute values will be slower than the increase of the CR import from the PRC.

Beyond Trade War: Reevaluating Intellectual Property Bilateralism in the US–China Context

2021 ◽  
Vol 24 (1) ◽  
pp. 53-76
Author(s):  
Carrie Shu Shang ◽  
Wei Shen
Keyword(s):  
Intellectual Property ◽  
Property Rights ◽  
Intellectual Property Rights ◽  
Trade Agreement ◽  
Republic Of China ◽  
China Trade ◽  
The Us ◽  
The Usa ◽  
Trade War ◽  
Organization Framework

ABSTRACT The Economic and Trade Agreement between the USA and the People’s Republic of China (hereinafter the ‘Phase One Agreement’) concluded in January 2020 leaves many important questions unanswered. This article goes beyond narrow textualist approaches and seeks to conceptualize the current trade tension by providing an alternative narrative with a focus on China’s post–Trade War commitments to higher intellectual property rights standards. In particular, it focuses on the bilateral interaction between the USA and China during and shortly after the Trade War and how the interaction impacts China’s legal changes from a transnational law perspective. It further argues that US-reinforced intellectual property rights rules have potentially paved the way for further US–China trade and investment talks. However, in order to better maintain a long-term balance between preservation of policymaking autonomy and regulation of protectionist measures, an approach better aligned with the World Trade Organization framework needs to be pursued.

scholarly journals Considering seasonal fluctuations on balancing time series with the use of artificial neural networks when forecasting US imports from the PRC

2020 ◽  
Vol 73 ◽  
pp. 01033
Author(s):  
Jaromír Vrbka ◽  
Marek Vochozka
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Seasonal Fluctuations ◽  
Republic Of China ◽  
The World Bank ◽  
Intelligence Models ◽  
The Usa ◽  
Artificial Intelligence Models ◽  
Artificial Neural

The paper’s objective is to propose a particular methodology to be used to regard seasonal fluctuations on balancing time series while using artificial neural networks based on the example of imports from the People's Republic of China (PRC) to the USA (US). The difficulty of forecasting the volume of foreign trade is usually given by the limitations of many conventional forecasting models. For the improvement of forecasting it is necessary to propose an approach that would hybridize econometric models and artificial intelligence models. Data for an analysis to be conducted are available on the World Bank website, etc. Information on US imports from the PRC will be used. Each forecast is given by a certain degree of probability which it will be fulfilled with. Although it appeared before the experiment that there was no reason to include the categorical variable to reflect seasonal fluctuations of the USA imports from the PRC, the assumption was not correct. An additional variable, in the form of monthly value measurements, brought greater order and accuracy to the balanced time series.

scholarly journals Machine learning forecasting of CR import from PRC in context of mutual PRC and USA sanctions

2020 ◽  
Vol 73 ◽  
pp. 01017
Author(s):  
Veronika Machová ◽  
Jan Mareček
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
International Trade ◽  
Success Rate ◽  
Future Development ◽  
Trade Restrictions ◽  
Mutual Trade ◽  
The Usa

Mutual trade restrictions between the USA and the PRC caused by the USA feeling of imbalance of trade between these two countries have significantly influenced not only the trade between these two states but also the overall atmosphere of the international trade in the last few years. The objective of the contribution is to find out whether machine learning forecasting is capable of equalizing time series so that the model effectively forecasts the future development of the time series even in the context of an extraordinary situation caused by such factors as the mutual sanctions of the USA and PRC. The dataset shows the course of the time series at monthly intervals starting from January 2000 to June 2019. There is regression carried out using neural structures. Three sets of artificial neural networks are generated. They are differ in the considered time series lag. 10,000 neural networks are generated, out of which 5 with the best characteristics are retained. The mutual USA and PRC sanctions did not affect the success rate of the machine learning forecasting of the CR import from the PRC. It is evident that the mutual sanctions shall affect the trade between the CR and the PRC.

scholarly journals Comparison of neural networks and regression time series in estimating the development of the EU and the PRC trade balance

2019 ◽  
Vol 61 ◽  
pp. 01031 ◽  
Author(s):  
Jaromír Vrbka ◽  
Zuzana Rowland ◽  
Petr Šuleř
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
International Relations ◽  
Trade Balance ◽  
Point Of View ◽  
Republic Of China ◽  
China Trade ◽  
The Neural Networks ◽  
Time Series Alignment ◽  
The Eu

China, by GDP, is the second largest economic power, and hence also a key player in the field of international relations. As far as the EU is concerned, it is China's largest trading partner. From this point of view, it is clear that monitoring export and import development between these partners is essential. This paper therefore aims to compare two useful methods, namely the accuracy of time series alignment through regression analysis and artificial neural networks, to assess the evolution of the EU and the People's Republic of China trade balance. Data on the export and import trends of these two partners since 2000 have been used, and it is clear that the trade balance was completely different that year than it is now. The development over time is interesting. The most appropriate curve is selected from the linear regression, and from the neural networks three useful neural structures are selected. We also look at the prediction of future developments while taking into account seasonal fluctuations.

scholarly journals Comparison of neural networks and regression time series in estimating the Czech Republic and China trade balance

2019 ◽  
Vol 61 ◽  
pp. 01023 ◽  
Author(s):  
Zuzana Rowland ◽  
Petr Šuleř ◽  
Marek Vochozka
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Czech Republic ◽  
Linear Regression ◽  
Trade Balance ◽  
People’S Republic Of China ◽  
People's Republic Of China ◽  
The Czech Republic ◽  
Republic Of China ◽  
China Trade

Foreign trade has been and is considered to be very important. Trade balance measurement provides one of the best analyzes of a country's external economic relations. It serves as a monetary expression of economic transactions between a certain country and its foreign partners over a certain period. The aim of this paper is to compare the accuracy of time series alignment by means of regression analysis and neural networks on the example of the trade balance of the Czech Republic and the People's Republic of China. Trade balance data between the Czech Republic and the People's Republic of China is used. This is a monthly balance starting in 2000 and ending in July 2018. First, a linear regression is made followed by regression using artificial neural networks. A comparison of both methods at expert level and experience of the evaluator, the economist, is performed. Optically, the LOWESS curve appears to be best out of the linear regression and the fifth preserved RBF 1-24-1 network seems the mot appropriate out of neural networks.

The Thucydides Trap as an Alternative Explanation for the US–China Trade War

2020 ◽  
Vol 12 (1) ◽  
pp. 42-55 ◽  
Author(s):  
Imad A. Moosa
Keyword(s):  
Alternative Explanation ◽  
Bilateral Trade ◽  
National Income ◽  
Trade Deficit ◽  
Basic Principles ◽  
China Trade ◽  
The Us ◽  
The Usa ◽  
Thucydides Trap ◽  
Trade War

The current trade war between the USA and China is perceived to be motivated by the US desire to curtail the bilateral trade deficit, on the assumption that reducing the deficit boosts economic growth. This flawed proposition indicates gross misunderstanding of the national income identity and the basic principles of macroeconomics. The imposition of tariffs will not reduce the trade deficit as the assumptions and conditions required for a smooth working of the process are unrealistic and counterfactual. The notion of an economic Thucydides trap is put forward to explain why the trade war is motivated by US apprehension about China’s rising economic power.

scholarly journals Forecasting COVID-19 confirmed cases, deaths and recoveries: Revisiting established time series modeling through novel applications for the USA and Italy

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244173
Author(s):  
Emrah Gecili ◽  
Assem Ziady ◽  
Rhonda D. Szczesniak
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Prediction Interval ◽  
Health Planning ◽  
State Space Model ◽  
Policy Decision ◽  
Prediction Errors ◽  
The Future ◽  
The Usa ◽  
Novel Applications

The novel coronavirus (COVID-19) is an emergent disease that initially had no historical data to guide scientists on predicting/ forecasting its global or national impact over time. The ability to predict the progress of this pandemic has been crucial for decision making aimed at fighting this pandemic and controlling its spread. In this work we considered four different statistical/time series models that are readily available from the ‘forecast’ package in R. We performed novel applications with these models, forecasting the number of infected cases (confirmed cases and similarly the number of deaths and recovery) along with the corresponding 90% prediction interval to estimate uncertainty around pointwise forecasts. Since the future may not repeat the past for this pandemic, no prediction model is certain. However, any prediction tool with acceptable prediction performance (or prediction error) could still be very useful for public-health planning to handle spread of the pandemic, and could policy decision-making and facilitate transition to normality. These four models were applied to publicly available data of the COVID-19 pandemic for both the USA and Italy. We observed that all models reasonably predicted the future numbers of confirmed cases, deaths, and recoveries of COVID-19. However, for the majority of the analyses, the time series model with autoregressive integrated moving average (ARIMA) and cubic smoothing spline models both had smaller prediction errors and narrower prediction intervals, compared to the Holt and Trigonometric Exponential smoothing state space model with Box-Cox transformation (TBATS) models. Therefore, the former two models were preferable to the latter models. Given similarities in performance of the models in the USA and Italy, the corresponding prediction tools can be applied to other countries grappling with the COVID-19 pandemic, and to any pandemics that can occur in future.

scholarly journals Machine prediction of US imports from the PRC in the context of mutual sanctions

2020 ◽  
Vol 73 ◽  
pp. 01027
Author(s):  
Petr Šuleř ◽  
Jan Mareček
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
United States Of America ◽  
The United States ◽  
Seasonal Fluctuations ◽  
Trade Restrictions ◽  
Republic Of China ◽  
The Usa ◽  
Multilayer Perceptron Networks ◽  
Excellent Tool

The aim of this paper is to mechanically predict the import of the United States of America (USA) from the People's Republic of China (PRC). The trade restrictions of the USA and the PRC caused by the USA feeling of imbalance of trade between the two states have significantly influenced not only the trade between the two players, but also the overall climate of international trade. The result of this paper is the finding that multilayer perceptron networks (MLP) appear to be an excellent tool for predicting USA imports from the PRC. MLP networks can capture both the trend of the entire time series and its seasonal fluctuations. It also emerged that time series delays need to be applied. Acceptable results are shown to delay series of the order of 5 and 10 months. The mutual sanctions of both countries did not have a significant impact on the outcome of the machine learning prediction.

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