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Research on grey systems is becoming more sophisticated, and grey relational and prediction analyses are receiving close review worldwide. Particularly, the application of grey systems in marine economics is gaining importance. The purpose of this paper is to summarize and review literature on grey models, providing new directions in their application in the marine economy.

This paper organized seminal studies on grey systems published by Chinese core journal database – CNKI, Web of Science and Elsevier from 1982 to 2018. After searching the aforementioned database for the said duration, the authors used the CiteSpace visualization tools to analyze them.

The authors sorted the studies according to their countries/regions, institutions, keywords and categories using the CiteSpace tool; analyzed current research characteristics on grey models; and discussed their possible applications in marine businesses, economy, scientific research and education, marine environment and disasters. Finally, the authors pointed out the development trend of grey models.

Although researches are combining grey theory with fractals, neural networks, fuzzy theory and other methods, the applications, in terms of scope, have still not met the demand. With the increasingly in-depth research in marine economics and management, international marine economic research has entered a new period of development. Grey theory will certainly attract scholars’ attention, and its role in marine economy and management will gain considerable significance.

This study aims to use gray models to predict abnormal stock returns.

Data are collected from listed companies in the Tehran Stock Exchange during 2005-2015. The analyses portray three models, namely, the gray model, the nonlinear gray Bernoulli model and the Nash nonlinear gray Bernoulli model.

Results show that the Nash nonlinear gray Bernoulli model can predict abnormal stock returns that are defined by conditions other than gray models which predict increases, and then after checking regression models, the Bernoulli regression model is defined, which gives higher accuracy and fewer errors than the other two models.

The stock market is one of the most important markets, which is influenced by several factors. Thus, accurate and reliable techniques are necessary to help investors and consumers find detailed and exact ways to predict the stock market.

The purpose of this paper is to propose a framework for data analytics where everything is grey in nature and the associated uncertainty is considered as an essential part in data collection, profiling, imputation, analysis and decision making.

A comparative study is conducted between the available uncertainty models and the feasibility of grey systems is highlighted. Furthermore, a general framework for the integration of grey systems and grey sets into data analytics is proposed.

Grey systems and grey sets are useful not only for small data, but also big data as well. It is complementary to other models and can play a significant role in data analytics.

The proposed framework brings a radical change in data analytics. It may bring a fundamental change in our way to deal with uncertainties.

The proposed model has the potential to avoid the mistake from a misleading data imputation.

The proposed model takes the philosophy of grey systems in recognising the limitation of our knowledge which has significant implications in our way to deal with our social life and relations.

This is the first time that the whole data analytics is considered from the point of view of grey systems.

This paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.

Six different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.

The authors found that the grey forecast is a reliable forecasting method for crude oil prices.

The contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.

The occurrence and unpredictability of speculative bubbles on financial markets, and their accompanying crashes, have confounded economists and economic historians worldwide. The purpose of this paper is to diagnose and detect the bursting of shipping bubbles ex ante, and to qualify the patterns of shipping price dynamics and the bubble mechanics, so that appropriate counter measures can be taken in advance to reduce side effects arising from bubbles.

Log periodic power law (LPPL) model, developed in the past decade, is used to detect large market falls or “crashes” through modeling of the shipping price dynamics on a selection of three historical shipping bubbles over the period of 1985 to 2016. The method is based on a nonlinear least squares estimation that yields predictions of the most probable time of the regime switching.

It could be concluded that predictions by the LPPL model are quite dependent on the time at which they are conducted. Interestingly, the LPPL model could have predicted the substantial fall in the Baltic Dry Index during the recent global downturn, but not all crashes in the past. It is also found that the key ingredient that sets off an unsustainable growth process for shipping prices is the positive feedback. When the positive feedback starts, the burst of bubbles in shipping would be influenced by both endogenous and exogenous factors, which are crucial for the advanced warning of the market conversion.

The LPPL model has been first applied into the dry bulk shipping market to test a couple of shipping bubbles. The authors not only assess the predictability and robustness of the LPPL model but also expand the understanding of the model and explain patterns of shipping price dynamics and bubble mechanics.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

The purpose of this paper is to explore the internal interaction mechanism of marine scientific research and education, industrial structure upgrading and marine economic growth from a systematic perspective, based on which this work forecasts their future development trends.

In this study, a multivariate grey model is applied to the prediction of marine scientific research and education, industrial structure upgrading and marine economic growth. Considering the impact of the COVID-19 on marine development, this paper introduces the weakening buffer operator into MGM(1,m) and constructs the AWBO-MGM(1,m) model. To verify the validity and accuracy of the new model, this paper uses AWBO-MGM(1,m), MGM(1,m), GM(1,N), GM(1,1), back propagation neural network and linear regression models for simulation and prediction based on the data from 2010 to 2021, respectively.

From the theoretical perspective, the development of marine scientific research and education can accelerate industrial upgrading and promote marine economic growth by providing high-quality talents, promoting marine science and technology progress and reducing transaction costs; while the upgrading of marine industrial structure and marine economic growth can promote the development of marine scientific research and education by guiding social capital, enhancing talent demand and stimulating market vitality. From the empirical analysis, the AWBO-MGM(1,m) model can effectively deal with epidemic shocks and has higher fitting and prediction accuracy than the other five comparative models.

The government should pay attention to the construction of marine scientific research and education, so as to provide high-quality talents and advanced scientific research results for the high-quality development of marine economy. On the basis of using science and technology to firmly build the primary and secondary marine industries, the government should actively guide the labor, capital and other factors of production to the tertiary industry, thereby promoting the optimization and upgrading of marine industrial structure.

On the one hand, the interplay mechanism of marine scientific research and education, industrial structure upgrading and marine economic growth is analyzed from a systematic perspective; on the other hand, the enhanced AWBO-MGM(1,m) possesses higher forecasting performance and is applicable to the systemic multivariate forecasting problem in the presence of outstanding external shocks.