The short-term forecasting of evaporation duct height (EDH) based on ARIMA model

2016 ◽  
Vol 76 (23) ◽  
pp. 24903-24916 ◽  
Author(s):  
Shaobo Yang ◽  
Sihui Liu ◽  
Xingfei Li ◽  
Ying Zhong ◽  
Xin He ◽  
...  
Keyword(s):  
Arima Model ◽  
Short Term ◽  
Evaporation Duct ◽  
Short Term Forecasting ◽  
Evaporation Duct Height

scholarly journals Time Series Analysis of Evaporation Duct Height over South China Sea: A Stochastic Modeling Approach

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1663
Author(s):  
Fei Hong ◽  
Qi Zhang
Keyword(s):  
Time Series ◽  
South China Sea ◽  
Stochastic Modeling ◽  
South China ◽  
Arima Model ◽  
The South China Sea ◽  
The South ◽  
China Sea ◽  
Evaporation Duct ◽  
Evaporation Duct Height

The evaporation duct could significantly affect the work status of maritime microwave communication systems in the South China Sea. Therefore, the exact forecasting of the evaporation duct is vital for the normal operation of the systems. This study presents a stochastic modeling approach to predict the future trends of the evaporation duct over the South China Sea. The autoregressive integrated moving average (ARIMA) model has been used for modeling the monthly evaporation duct height estimated from the Climate Forecast System Reanalysis dataset released by the National Centers for Environment Prediction. The long-term evaporation duct height data were collected for a period of 10 years from 2008 to 2017. The analysis of correlation function reveals the existence of seasonality in the time series. Therefore, a seasonal ARIMA model with the form as ARIMA (0,0,1) × (0,1,2)12 is proposed by fitting the monthly data optimally. The fitted model is further used to forecast the evaporation duct variation for the year 2018 at 95% level of confidence, and high-accuracy results are obtained. Our study demonstrates the feasibility of the proposed stochastic modeling technique to predict the future variations of the evaporation duct over South China Sea.

scholarly journals A New Short-Term Prediction Method for Estimation of the Evaporation Duct Height

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 136036-136045
Author(s):  
Yanbo Mai ◽  
Zheng Sheng ◽  
Hanqing Shi ◽  
Chaolei Li ◽  
Qixiang Liao ◽  
...  
Keyword(s):  
Prediction Method ◽  
Short Term ◽  
Evaporation Duct ◽  
Term Prediction ◽  
Evaporation Duct Height ◽  
Short Term Prediction

scholarly journals Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds

2021 ◽  
Vol 11 (20) ◽  
pp. 9441
Author(s):  
Tianyou Tao ◽  
Peng Shi ◽  
Hao Wang ◽  
Lin Yuan ◽  
Sheng Wang
Keyword(s):  
Nonlinear Models ◽  
Arima Model ◽  
Tropical Storm ◽  
Tropical Storms ◽  
Short Term ◽  
Wind Speeds ◽  
One Step ◽  
Linear And Nonlinear ◽  
Short Term Forecasting ◽  
Forecasting Errors

Wind-sensitive structures usually suffer from violent vibrations or severe damages under the action of tropical storms. It is of great significance to forecast tropical-storm winds in advance for the sake of reducing or avoiding consequent losses. The model used for forecasting becomes a primary concern in engineering applications. This paper presents a performance evaluation of linear and nonlinear models for the short-term forecasting of tropical storms. Five extensively employed models are adopted to forecast wind speeds using measured samples from the tropical storm Rumbia, which facilitates a comparison of the predicting performances of different models. The analytical results indicate that the autoregressive integrated moving average (ARIMA) model outperforms the other models in the one-step ahead prediction and presents the least forecasting errors in both the mean and maximum wind speeds. However, the support vector regression (SVR) model has the worst performance on the selected dataset. When it comes to the multi-step ahead forecasting, the prediction error of each model increases as the number of steps expands. Although each model shows an insufficient ability to capture the variation of future wind speed, the ARIMA model still appears to have the least forecasting errors. Hence, the ARIMA model can offer effective short-term forecasting of tropical-storm winds in both one-step and multi-step scenarios.

The causes of poverty in Russia

2020 ◽  
Vol 13 (1) ◽  
pp. 21-36
Author(s):  
I.S. Ivanchenko
Keyword(s):  
Regression Analysis ◽  
Regional Development ◽  
Financial Market ◽  
Russian Federation ◽  
Short Term ◽  
Financial Situation ◽  
Macroeconomic Variables ◽  
Development Plans ◽  
The Russian Federation ◽  
Short Term Forecasting

Subject. This article analyzes the changes in poverty of the population of the Russian Federation. Objectives. The article aims to identify macroeconomic variables that will have the most effective impact on reducing poverty in Russia. Methods. For the study, I used the methods of logical, comparative, and statistical analyses. Results. The article presents a list of macroeconomic variables that, according to Western scholars, can influence the incomes of the poorest stratum of society and the number of unemployed in the country. The regression analysis based on the selected variables reveals those ones that have a statistically significant impact on the financial situation of the Russian poor. Relevance. The results obtained can be used by the financial market mega-regulator to make anti-poverty decisions. In addition, the models built can be useful to the executive authorities at various levels for short-term forecasting of the number of unemployed and their income in drawing up regional development plans for the areas.

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model
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