scholarly journals GREY FORECASTING MODEL IMPLEMENTATION FOR FORECAST OF CAPTURED FISHERIES PRODUCTION

Kursor ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 169
Author(s):  
Muhammad Shodiq
Keyword(s):  
Weather Conditions ◽  
Forecast Model ◽  
Fishing Ground ◽  
Forecasting Model ◽  
Fishing Gear ◽  
Short Term ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Forecast Models

The increasing need for fish causes problems related to production in the fisheries sector. In fisheries production all information related to (fishing ground) is well known, but on the other hand it is not easy to predict the amount of production due to unclear information. This is also related to the number of ships that make trips, the length (time) of the trip, the type of fishing gear, weather conditions, the quality of human resources, natural environmental factors, and others. The purpose of this study is to apply Grey forecasting model or GM (1,1) to predict fisheries production. Grey forecasting models are used to build forecast models with limited amounts of data with short-term forecasts that will produce accurate forecasts. This study employs the data of captured fish from 2010 to 2018 to analyze calculations using the GM model (1,1). The results showed that the Grey forecasting model or GM (1.1) produced accurate forecasts with an ARPE error value of 9.60% or the accuracy of the forecast model reached 90.39%.

A comparative study of univariate models for container throughput forecasting of major ports in Asia

2021 ◽  
pp. 147509022110236
Author(s):  
Juan Huang ◽  
Ching-Wu Chu ◽  
Hsiu-Li Hsu
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Forecasting Model ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Port Authorities ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Short Term Forecasting ◽  
Trigonometric Regression

This study aims to make comparisons on different univariate forecasting methods and provides a more accurate short-term forecasting model on the container throughput for rendering a reference to relevant authorities. We collected monthly data regarding container throughput volumes for three major ports in Asia, Shanghai, Singapore, and Busan Ports. Six different univariate methods, including the grey forecasting model, the hybrid grey forecasting model, the multiplicative decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, and the seasonal autoregressive integrated moving average (SARIMA) model, were used. We found that the hybrid grey forecasting model outperforms the other univariate models. This study’s findings can provide a more accurate short-term forecasting model for container throughput to create a reference for port authorities.

Study of a discrete grey forecasting model based on the quality cost characteristic curve

2017 ◽  
Vol 7 (3) ◽  
pp. 376-384 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Xiaoyu Yang ◽  
Qian Hu ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Characteristic Curve ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Quality Level ◽  
Cost Models ◽  
Quality Cost ◽  
Content Type ◽  
Grey Forecasting ◽  
Grey Forecasting Model

Purpose The purpose of this paper is to clarify several commonly used quality cost models based on Juran’s characteristic curve. Through mathematical deduction, the lowest point of quality cost and the lowest level of quality level (often depicted by qualification rate) can be obtained. This paper also aims to introduce a new prediction model, namely discrete grey model (DGM), to forecast the changing trend of quality cost. Design/methodology/approach This paper comes to the conclusion by means of mathematical deduction. To make it more clear, the authors get the lowest quality level and the lowest quality cost by taking the derivative of the equation of quality cost and quality level. By introducing the weakening buffer operator, the authors can significantly improve the prediction accuracy of DGM. Findings This paper demonstrates that DGM can be used to forecast quality cost based on Juran’s cost characteristic curve, especially when the authors do not have much information or the sample capacity is rather small. When operated by practical weakening buffer operator, the randomness of time series can be obviously weakened and the prediction accuracy can be significantly improved. Practical implications This paper uses a real case from a literature to verify the validity of discrete grey forecasting model, getting the conclusion that there is a certain degree of feasibility and rationality of DGM to forecast the variation tendency of quality cost. Originality/value This paper perfects the theory of quality cost based on Juran’s characteristic curve and expands the scope of application of grey system theory.

scholarly journals A Dynamic Forecasting Model for Scientific Migration in the Region

2021 ◽  
Vol 17 (4) ◽  
pp. 1196-1209
Author(s):  
A.E. Sudakova ◽  
◽  
A.A. Tarasyev ◽  
D.G. Sandler ◽  
◽  
...  
Keyword(s):  
Quality Of Life ◽  
Economic Benefits ◽  
Forecasting Model ◽  
Short Term ◽  
Migration Factors ◽  
Dynamic Forecasting ◽  
Migration Flows ◽  
Key Indicators ◽  
Scientific Migration

The population migration has attracted attention for more than a decade. As migration consequences differ in terms of characteristics and directions, governments worldwide are looking for solutions to regulate migration flows. The study aims to systematise push-pull factors of migration by analysing existing cases, as well as to build a model for predicting migration considering the quantitative interpretation of such factors. While migration factors are quite similar regardless of the country of residence, their main differences are compatibility and hierarchy. The most frequently mentioned factors include the expectation of income increase, improvement in the quality of life, professional aspects. Simultaneously, a certain pattern emerges: if a migrant’s material and economic needs are satisfied in the country of departure, they pay more attention to intangible/non-economic benefits (quality of life, infrastructure, etc.). A dynamic forecasting model for scientific migration has been developed based on the theory of positional games. The model demonstrates the changes in migration flows by describing the behaviour of a rational individual who seeks to maximise benefits from migration. The result of the simulation is a short-term forecast of trends in scientific migration of Ural scholars to key migration countries. The model predicts the intensification of migration flows to the leading Asian countries, their alignment with flows to America, and a decrease in migration to European countries. This forecast is characterised by a direct dependence of the dynamics of scientific migration flows on the socio-economic development of migration destinations. Practical implications of this study include the development of a predictive model describing migration flows in the short term as an analytical tool and systematisation of pull-push factors as key indicators for managing the migration flows of scientists. In addition, the research proposes measures positively affecting the balance of scientific migration.

Vulnerability of hydropower generation to climate change in China: Results based on Grey forecasting model

Energy Policy ◽  
2014 ◽  
Vol 65 ◽  
pp. 701-707 ◽  
Author(s):  
Bing Wang ◽  
Xiao-Jie Liang ◽  
Hao Zhang ◽  
Lu Wang ◽  
Yi-Ming Wei
Keyword(s):  
Climate Change ◽  
Forecasting Model ◽  
Hydropower Generation ◽  
Grey Forecasting ◽  
Grey Forecasting Model
Sign in / Sign up

Export Citation Format

Share Document