For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compare...For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compared with the type-1 TSK fuzzy logic system method,interval type-2 fuzzy sets could simultaneously model both intra-personal uncertainty and inter-personal uncertainty based on the training of the existing error back propagation(BP)algorithm,and the IGWO algorithm was used for training the model premise and consequent parameters to further improve the predictive performance of the model.By improving the gray wolf optimization algorithm,the early convergence judgment mechanism,nonlinear cosine adjustment strategy,and Levy flight strategy were introduced to improve the convergence speed of the algorithm and avoid the problem of falling into local optimum.The interval type-2 TSK FLS method based on the IGWO algorithm was applied to the real-world photovoltaic power time series forecasting instance.Under the same conditions,it was also compared with different IT2 TSK FLS methods,such as type I TSK FLS method,BP algorithm,genetic algorithm,differential evolution,particle swarm optimization,biogeography optimization,gray wolf optimization,etc.Experimental results showed that the proposed method based on IGWO algorithm outperformed other methods in performance,showing its effectiveness and application potential.展开更多
In recent work,three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation(DSAEF_LTP model)have been introduced,namely,tropical cyclone(TC)track,TC landfall...In recent work,three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation(DSAEF_LTP model)have been introduced,namely,tropical cyclone(TC)track,TC landfall season,and TC intensity.In the present study,we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added.Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018.The first experiment was designed as the DSAEF_LTP model with TC track,TC landfall season,and intensity(DSAEF_LTP-1).The other three experiments were based on the first experiment,but with new ensemble forecast schemes added(DSAEF_LTP-2),new similarity regions added(DSAEF_LTP-3),and both added(DSAEF_LTP-4),respectively.Results showed that,after new similarity regions added into the model(DSAEF_LTP-3),the forecasting performance of the DSAEF_LTP model for heavy rainfall(accumulated precipitation≥250 mm and≥100 mm)improved,and the sum of the threat score(TS250+TS100)increased by 4.44%.Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1,the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously(DSAEF_LTP-4),with the TS increasing by 25.36%.Moreover,the forecasting performance of the four experiments was compared with four operational numerical weather prediction models,and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall.Finally,some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed.展开更多
This study examines the statistical properties required to model the dynamics of both the returns and volatility series of the daily stock market returns in six Gulf Cooperation Council countries,namely Bahrain,Oman,K...This study examines the statistical properties required to model the dynamics of both the returns and volatility series of the daily stock market returns in six Gulf Cooperation Council countries,namely Bahrain,Oman,Kuwait,Qatar,Saudi Arabia,and the United Arab Emirates,under different financial and economic circumstances.The empiri-cal investigation is conducted using daily data from June 1,2005 to July 1,2019.The analysis is conducted using a set of double long-memory specifications with some significant features such as long-range dependencies,asymmetries in conditional variances,non-linearity,and multiple seasonality or time-varying correlations.Our study indicates that the joint dual long-memory process can adequately estimate long-memory dynamics in returns and volatility.The in-sample diagnostic tests as well as out-of-sample forecasting results demonstrate the prevalence of the Autoregressive Fractionally Integrated Moving Average and Hyperbolic Asymmetric Power Autoregressive Conditional Heteroskedasticity modeling process over other competing models in fitting the first and the second conditional moments of the market returns.Moreover,the empirical results show that the proposed model offers an interesting framework to describe the long-range dependence in returns and seasonal persistence to shocks in conditional volatility and strongly support the estimation of dynamic returns that allow for time-varying correlations.A noteworthy finding is that the long-memory dependencies in the conditional variance processes of stock market returns appear important,asymmetric,and differ in their volatility responses to unexpected shocks.Our evidence suggests that these markets are not completely efficient in processing regional news,thus providing a sound alternative for regional portfolio diversification.展开更多
According to eastmoney.com’s Choice data,50listed non-ferrous metals companies have announced the 2017 performance forecasts,where 39 companies forecast profits,accounting for more than 70%.Among them,there are 19 co...According to eastmoney.com’s Choice data,50listed non-ferrous metals companies have announced the 2017 performance forecasts,where 39 companies forecast profits,accounting for more than 70%.Among them,there are 19 companies each with a forecast net profit of more than RMB 100million and 13 companies with a more展开更多
Accurate pavement performance prediction plays a critical role in formulating maintenance and repair strategies for transportation departments,enabling the achievement of better pavement performance with limited finan...Accurate pavement performance prediction plays a critical role in formulating maintenance and repair strategies for transportation departments,enabling the achievement of better pavement performance with limited financial resources.However,due to the intricate influence of numerous factors on pavement performance deterioration,improving the accuracy of pavement performance prediction poses a challenge for conventional models.Therefore,the aim of this study is to establish a machine learning-based pavement performance prediction model.First,this study considers five factors that affect pavement performance,including pavement initial performance indicators,traffic loads,weather,pavement structure,and maintenance measures,and identifies 15 specific indicators that affect pavement performance based on these five factors.Then,based on the the long-term pavement performance(LTPP)database,the study screens and summarizes these indicators,obtaining 2464 high-quality pavement performance data for pavement conditions index(PCI)prediction and 3238 high-quality pavement performance data for international roughness index(IRI)prediction.Finally,three distinct prediction models are established,namely,the fully connected neural network(FCNN)model,the long short-term memory(LSTM)model,and the combined LSTM-attention model.The study shows that the LSTM-attention model performs significantly better than the FCNN and LSTM models,with an R2 coefficient of determination of 0.81 for PCI and 0.79 for IRI.The innovation of this paper is that the authors have introduced the attention mechanism on the basic of the LSTM model,which makes the fitting accuracy of the prediction model further improved.展开更多
基金supported by National Natural Science Foundation of China(No.12172157)Key Project of Natural Science Foundation of Gansu Province(No.25JRRA150)Key Research and Development Planning Project of Gansu Province(No.23YFWA0007).
文摘For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compared with the type-1 TSK fuzzy logic system method,interval type-2 fuzzy sets could simultaneously model both intra-personal uncertainty and inter-personal uncertainty based on the training of the existing error back propagation(BP)algorithm,and the IGWO algorithm was used for training the model premise and consequent parameters to further improve the predictive performance of the model.By improving the gray wolf optimization algorithm,the early convergence judgment mechanism,nonlinear cosine adjustment strategy,and Levy flight strategy were introduced to improve the convergence speed of the algorithm and avoid the problem of falling into local optimum.The interval type-2 TSK FLS method based on the IGWO algorithm was applied to the real-world photovoltaic power time series forecasting instance.Under the same conditions,it was also compared with different IT2 TSK FLS methods,such as type I TSK FLS method,BP algorithm,genetic algorithm,differential evolution,particle swarm optimization,biogeography optimization,gray wolf optimization,etc.Experimental results showed that the proposed method based on IGWO algorithm outperformed other methods in performance,showing its effectiveness and application potential.
基金National Key R&D Program of China(2019YFC1510205)Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province(SCSF202202)+1 种基金Shenzhen Science and Technology Project(KCXFZ2020122173610028)Jiangsu Collaborative Innovation Center for Climate Change。
文摘In recent work,three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation(DSAEF_LTP model)have been introduced,namely,tropical cyclone(TC)track,TC landfall season,and TC intensity.In the present study,we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added.Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018.The first experiment was designed as the DSAEF_LTP model with TC track,TC landfall season,and intensity(DSAEF_LTP-1).The other three experiments were based on the first experiment,but with new ensemble forecast schemes added(DSAEF_LTP-2),new similarity regions added(DSAEF_LTP-3),and both added(DSAEF_LTP-4),respectively.Results showed that,after new similarity regions added into the model(DSAEF_LTP-3),the forecasting performance of the DSAEF_LTP model for heavy rainfall(accumulated precipitation≥250 mm and≥100 mm)improved,and the sum of the threat score(TS250+TS100)increased by 4.44%.Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1,the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously(DSAEF_LTP-4),with the TS increasing by 25.36%.Moreover,the forecasting performance of the four experiments was compared with four operational numerical weather prediction models,and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall.Finally,some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed.
文摘This study examines the statistical properties required to model the dynamics of both the returns and volatility series of the daily stock market returns in six Gulf Cooperation Council countries,namely Bahrain,Oman,Kuwait,Qatar,Saudi Arabia,and the United Arab Emirates,under different financial and economic circumstances.The empiri-cal investigation is conducted using daily data from June 1,2005 to July 1,2019.The analysis is conducted using a set of double long-memory specifications with some significant features such as long-range dependencies,asymmetries in conditional variances,non-linearity,and multiple seasonality or time-varying correlations.Our study indicates that the joint dual long-memory process can adequately estimate long-memory dynamics in returns and volatility.The in-sample diagnostic tests as well as out-of-sample forecasting results demonstrate the prevalence of the Autoregressive Fractionally Integrated Moving Average and Hyperbolic Asymmetric Power Autoregressive Conditional Heteroskedasticity modeling process over other competing models in fitting the first and the second conditional moments of the market returns.Moreover,the empirical results show that the proposed model offers an interesting framework to describe the long-range dependence in returns and seasonal persistence to shocks in conditional volatility and strongly support the estimation of dynamic returns that allow for time-varying correlations.A noteworthy finding is that the long-memory dependencies in the conditional variance processes of stock market returns appear important,asymmetric,and differ in their volatility responses to unexpected shocks.Our evidence suggests that these markets are not completely efficient in processing regional news,thus providing a sound alternative for regional portfolio diversification.
文摘According to eastmoney.com’s Choice data,50listed non-ferrous metals companies have announced the 2017 performance forecasts,where 39 companies forecast profits,accounting for more than 70%.Among them,there are 19 companies each with a forecast net profit of more than RMB 100million and 13 companies with a more
基金supported by the Science and Technology Plan of Shandong Transportation Department(No.2021B47)the Key Research and Development Program of Ningxia Science and Technology Department(No.2022BEG02008)the Fundamental Research Funds for the Central Universities(No.22120210027).
文摘Accurate pavement performance prediction plays a critical role in formulating maintenance and repair strategies for transportation departments,enabling the achievement of better pavement performance with limited financial resources.However,due to the intricate influence of numerous factors on pavement performance deterioration,improving the accuracy of pavement performance prediction poses a challenge for conventional models.Therefore,the aim of this study is to establish a machine learning-based pavement performance prediction model.First,this study considers five factors that affect pavement performance,including pavement initial performance indicators,traffic loads,weather,pavement structure,and maintenance measures,and identifies 15 specific indicators that affect pavement performance based on these five factors.Then,based on the the long-term pavement performance(LTPP)database,the study screens and summarizes these indicators,obtaining 2464 high-quality pavement performance data for pavement conditions index(PCI)prediction and 3238 high-quality pavement performance data for international roughness index(IRI)prediction.Finally,three distinct prediction models are established,namely,the fully connected neural network(FCNN)model,the long short-term memory(LSTM)model,and the combined LSTM-attention model.The study shows that the LSTM-attention model performs significantly better than the FCNN and LSTM models,with an R2 coefficient of determination of 0.81 for PCI and 0.79 for IRI.The innovation of this paper is that the authors have introduced the attention mechanism on the basic of the LSTM model,which makes the fitting accuracy of the prediction model further improved.
