The energy sector is the second largest emitter of greenhouse (GHG) gases in Kenya, emitting about 31.2% of GHG emissions in the country. The aim of this study was to model Kenya’s GHG emissions by the energy sector ...The energy sector is the second largest emitter of greenhouse (GHG) gases in Kenya, emitting about 31.2% of GHG emissions in the country. The aim of this study was to model Kenya’s GHG emissions by the energy sector using ARIMA models for forecasting future values. The data used for the study was that of Kenya’s GHG emissions by the energy sector for the period starting from 1970 to 2022 obtained for the International Monetary Fund (IMF) database that was split into training and testing sets using the 80/20 rule for modelling purposes. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE). ARIMA (1, 1, 1) was identified as the best model for modelling Kenya’s GHG emissions and forecasting future values. Using this model, Kenya’s GHG emissions by the energy sector were forecasted to increase to a value of about 43.13 million metric tons of carbon dioxide equivalents by 2030. The study, therefore, recommends that Kenya should accelerate the adjustment of industry structure and improve the efficient use of energy, optimize the energy structure and accelerate development and promotion of energy-efficient products to reduce the emission of GHGs by the country’s energy sector.展开更多
Oceanic turbulence measurements made by an acoustic Doppler velocimeter(ADV)suffer from noise that potentially affects the estimates of turbulence statistics.This study examines the abilities of Kalman filtering and a...Oceanic turbulence measurements made by an acoustic Doppler velocimeter(ADV)suffer from noise that potentially affects the estimates of turbulence statistics.This study examines the abilities of Kalman filtering and autoregressive moving average models to eliminate noise in ADV velocity datasets of laboratory experiments and offshore observations.Results show that the two methods have similar performance in ADV de-noising,and both effectively reduce noise in ADV velocities,even in cases of high noise.They eliminate the noise floor at high frequencies of the velocity spectra,leading to a longer range that effectively fits the Kolmogorov-5/3 slope at midrange frequencies.After de-noising adopting the two methods,the values of the mean velocity are almost unchanged,while the root-mean-square horizontal velocities and thus turbulent kinetic energy decrease appreciably in these experiments.The Reynolds stress is also affected by high noise levels,and de-noising thus reduces uncertainties in estimating the Reynolds stress.展开更多
Objective:Near vision loss(NVL)is one of the leading causes of visual impairment worldwide,exerting a profound impact on individual quality of life and socio-economic development.This study aims to analyze the burden ...Objective:Near vision loss(NVL)is one of the leading causes of visual impairment worldwide,exerting a profound impact on individual quality of life and socio-economic development.This study aims to analyze the burden of NVL in China by sex and age groups from 1990 to 2021 and to project trends over the next 15 years.Methods:Using data from the Global Burden of Disease(GBD)2021 database,we conducted descriptive analyses of NVL prevalence in China,calculated age-standardized prevalence rates(ASPR)and age-standardized disability-adjusted life years rates(ASDR)to compare burden differences between sexes and age groups,and applied an autoregressive integrated moving average(ARIMA)model to predict NVL trends for the next 15 years.The model selection was based on best-fit criteria to ensure reliable projections.Results:From 1990 to 2021,China’s ASPR of NVL rose from 10096.24/100000 to 15624.54/100000,and ASDR increased from 101.75/100000 to 158.75/100000.In 2021,ASPR(16551.70/100000)and ASDR(167.69/100000)were higher among females than males(14686.21/100000 and 149.76/100000,respectively).China ranked highest globally in both NVL cases and disability-adjusted life years(DALYs),with female burden significantly exceeding male burden.Projections indicated this trend and sex gap will persist until 2036.Compared with 1990,the prevalence cases and DALYs increased by 239.20%and 238.82%,respectively in 2021,with the highest burden among females and the 55−59 age group.The ARIMA model predicted continued increases in prevalence and DALYs by 2036,with females maintaining a higher burden than males.Conclusion:This study reveals a marked increase in the NVL burden in China and predicts continued growth in the coming years.Public health policies should prioritize NVL prevention and control,with special attention to women and middle-aged populations to mitigate long-term societal and health impacts.展开更多
This paper presents a method combining B-splines and autoregressive moving averages for the joint correction of sound velocity disturbances,addressing the problem of existing parameterized models for sound velocity er...This paper presents a method combining B-splines and autoregressive moving averages for the joint correction of sound velocity disturbances,addressing the problem of existing parameterized models for sound velocity errors in underwater geodetic positioning without considering the temporal correlation of sound velocity disturbances.Initially,a quadratic polynomial and a cubic B-spline model are utilized to preliminarily correct the disturbed sound speed structure.Subsequently,considering temporal correlation variations,sound speed error processing is conducted based on the autoregressive moving average model.Finally,the method is validated using data from the 3000-meter sea trial in the South China Sea.The results indicate that,compared to the quadratic polynomial and cubic B-spline sound speed corrections,applying the autoregressive moving average model for successive sound speed correction reduces the root mean square error of time observation value residuals by 58%and 30%,respectively.展开更多
This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characte...This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.展开更多
For the low utilization rate of photovoltaic power generation,taking a new energy power system constisting of concentrating solar power(CSP),photovoltaic power(PP)and battery energy storage system as an example,a mult...For the low utilization rate of photovoltaic power generation,taking a new energy power system constisting of concentrating solar power(CSP),photovoltaic power(PP)and battery energy storage system as an example,a multi-objective optimization scheduling strategy considering energy storage participation is proposed.Firstly,the new energy power system model is established,and the PP scenario generation and reduction frame based on the autoregressive moving average model and Kantorovich-distance is proposed.Then,based on the optimization goal of the system operation cost minimization and the PP output power consumption maximization,the multi-objective optimization scheduling model is established.Finally,the simulation results show that introducing energy storage into the system can effectively reduce the system operation cost and improve the utilization efficiency of PP.展开更多
China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity.The country has entered a new development stage designed to achieve its second centenary goal.However,ecological fragil...China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity.The country has entered a new development stage designed to achieve its second centenary goal.However,ecological fragility and risk susceptibility have increased the risk of returning to ecological poverty.In this paper,the Liupan Mountain Region of China was used as a case study,and the counties were used as the scale to reveal the spatiotempora differentiation and influcing factors of the risk of returning to poverty in study area.The indicator data for returning to ecological poverty from 2011-2020 were collected and summarized in three dimensions:ecological,economic and social.The autoregressive integrated moving average model(ARIMA)time series and exponential smoothing method(ES)were used to predict the multidimensional indicators of returning to ecological poverty for 61 counties(districts)in the Liupan Mountain Region for 2021-2030.The back propagation neural network(BPNN)and geographic information system(GIS)were used to generate the spatial distribution and time variation for the index of the risk of returning to ecological poverty(RREP index).The results show that 1)ecological factors were the main factors in the risk of returning to ecological poverty in Liupan Mountain Region.2)The RREP index for the 61 counties(districts)exhibited a downward trend from 2021-2030.The RREP index declined more in medium-and high-risk areas than in low-risk areas.From 2021 to 2025,the RREP index exhibited a slight downward trend.From 2026 to2030,the RREP index was expected to decline faster,especially from 2029-2030.3)Based on the RREP index,it can be roughly divided into three types,namely,the high-risk areas,the medium-risk areas,and the low-risk areas.The natural resource conditions in lowrisk areas of returning to ecological poverty,were better than those in medium-and high-risk areas.展开更多
Time series analysis has two goals, modeling random mechanisms and predicting future series using historical data. In the present work, a uni-variate time series autoregressive integrated moving average （ARIMA） mode...Time series analysis has two goals, modeling random mechanisms and predicting future series using historical data. In the present work, a uni-variate time series autoregressive integrated moving average （ARIMA） model has been developed for （a） simulating and forecasting mean rainfall, obtained using Theissen weights; over the Mahanadi River Basin in India, and （b） simula^ag and forecasting mean rainfall at 38 rain-gauge stations in district towns across the basin. For the analysis, monthly rainfall data of each district town for the years 1901-2002 （102 years） were used. Theissen weights were obtained over the basin and mean monthly rainfall was estimated. The trend and seasonality observed in ACF and PACF plots of rainfall data were removed using power transformation （a=0.5） and first order seasonal differencing prior to the development of the ARIMA model. Interestingly, the AR1MA model （1,0,0）（0,1,1）12 developed here was found to be most suitable for simulating and forecasting mean rainfall over the Mahanadi River Basin and for all 38 district town rain-gauge stations, separately. The Akaike Information Criterion （AIC）, good- ness of fit （Chi-square）, R2 （coefficient of determination）, MSE （mean square error） and MAE （mea absolute error） were used to test the validity and applicability of the developed ARIMA model at different stages. This model is considered appropriate to forecast the monthly rainfall for the upcoming 12 years in each district town to assist decision makers and policy makers establish priorities for water demand, storage, distribution, and disaster management.展开更多
Based on dynamic response signals a damage detection algorithm is developed for marine risers. Damage detection methods based on numerous modal properties have encountered issues in the researches in offshore oil comm...Based on dynamic response signals a damage detection algorithm is developed for marine risers. Damage detection methods based on numerous modal properties have encountered issues in the researches in offshore oil community. For example, significant increase in structure mass due to marine plant/animal growth and changes in modal properties by equipment noise are not the result of damage for riser structures. In an attempt to eliminate the need to determine modal parameters, a data-based method is developed. The implementation of the method requires that vibration data are first standardized to remove the influence of different loading conditions and the autoregressive moving average(ARMA) model is used to fit vibration response signals. In addition, a damage feature factor is introduced based on the autoregressive(AR) parameters. After that, the Euclidean distance between ARMA models is subtracted as a damage indicator for damage detection and localization and a top tensioned riser simulation model with different damage scenarios is analyzed using the proposed method with dynamic acceleration responses of a marine riser as sensor data. Finally, the influence of measured noise is analyzed. According to the damage localization results, the proposed method provides accurate damage locations of risers and is robust to overcome noise effect.展开更多
Existing detection methods against SYN flooding attacks are effective only at the later stages when attacking signatures are obvious.In this paper an early stage detecting method(ESDM) is proposed.The ESDM is a simple...Existing detection methods against SYN flooding attacks are effective only at the later stages when attacking signatures are obvious.In this paper an early stage detecting method(ESDM) is proposed.The ESDM is a simple but effective method to detect SYN flooding attacks at the early stage.In the ESDM the SYN traffic is forecasted by autoregressive integrated moving average model, and non-parametric cumulative sum algorithm is used to find the SYN flooding attacks according to the forecasted traffic.Trace-driven simulations show that ESDM is accurate and efficient to detect the SYN flooding attacks.展开更多
In the paper,the autoregressive moving average model for matrix time series(MARMA)is inves-tigated.The properties of the MARMA model are investigated by using the conditional least square estimation,the conditional ma...In the paper,the autoregressive moving average model for matrix time series(MARMA)is inves-tigated.The properties of the MARMA model are investigated by using the conditional least square estimation,the conditional maximum likelihood estimation,the projection theorem in Hilbert space and the decomposition technique of time series,which include necessary and suf-ficient conditions for stationarity and invertibility,model parameter estimation,model testing and model forecasting.展开更多
The stock market is a vital component of the broader financial system,with its dynamics closely linked to economic growth.The challenges associated with analyzing and forecasting stock prices have persisted since the ...The stock market is a vital component of the broader financial system,with its dynamics closely linked to economic growth.The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets.By examining historical transaction data,latent opportunities for profit can be uncovered,providing valuable insights for both institutional and individual investors to make more informed decisions.This study focuses on analyzing historical transaction data from four banks to predict closing price trends.Various models,including decision trees,random forests,and Long Short-Term Memory(LSTM)networks,are employed to forecast stock price movements.Historical stock transaction data serves as the input for training these models,which are then used to predict upward or downward stock price trends.The study’s empirical results indicate that these methods are effective to a degree in predicting stock price movements.The LSTM-based deep neural network model,in particular,demonstrates a commendable level of predictive accuracy.This conclusion is reached following a thorough evaluation of model performance,highlighting the potential of LSTM models in stock market forecasting.The findings offer significant implications for advancing financial forecasting approaches,thereby improving the decision-making capabilities of investors and financial institutions.展开更多
The realization of road traffic prediction not only provides real-time and effective information for travelers, but also helps them select the optimal route to reduce travel time. Road traffic prediction offers traffi...The realization of road traffic prediction not only provides real-time and effective information for travelers, but also helps them select the optimal route to reduce travel time. Road traffic prediction offers traffic guidance for travelers and relieves traffic jams. In this paper, a real-time road traffic state prediction based on autoregressive integrated moving average （ARIMA） and the Kalman filter is proposed. First, an ARIMA model of road traffic data in a time series is built on the basis of historical road traffic data. Second, this ARIMA model is combined with the Kalman filter to construct a road traffic state prediction algorithm, which can acquire the state, measurement, and updating equations of the Kalman filter. Third, the optimal parameters of the algorithm are discussed on the basis of historical road traffic data. Finally, four road segments in Beijing are adopted for case studies. Experimental results show that the real-time road traffic state prediction based on ARIMA and the Kalman filter is feasible and can achieve high accuracy.展开更多
Proper regulation of the earth pressure on the bulkhead of earth-pressure balanced(EPB)shield tunneling machines is significant to ensure safe construction.This study proposes a procedure for regulating the bulkhead p...Proper regulation of the earth pressure on the bulkhead of earth-pressure balanced(EPB)shield tunneling machines is significant to ensure safe construction.This study proposes a procedure for regulating the bulkhead pressure by combining numerical simulations and data mining,and applies the procedure to a metro line constructed in sandy pebble stratum using EPB shield.Firstly,the relationship between the bulkhead pressure and the pressure on the tunnel face is carefully obtained from discrete element modeling,and the required supporting earth pressure is derived by considering the arching effect.Secondly,aided with the machine learning method,a model is constructed for predicting the average bulkhead pressure per ring according to the operational parameters(i.e.,the average driving speed and the rotation speed of the screw conveyor).Given the target value of the bulkhead pressure,the optimal values of the operational parameters are obtained from the model.In addition,an autoregressive moving average stochastic process model is developed to monitor the real-time fluctuation of the bulkhead pressure and guide the actions taken in time to avoid dramatic fluctuations.The results indicate that the pressure difference between the tunnel face and the bulkhead is considerable,and the consideration of the arching effect can avoid overestimating the bulkhead pressure.A combination of the machine learning model and the stochastic process model provides a plausible performance in regulating the bulkhead pressure around the target value without dramatic fluctuation.展开更多
Electricity prices have complex features,such as high frequency,multiple seasonality,and nonlinearity.These factors will make the prediction of electricity prices difficult.However,accurate electricity price predictio...Electricity prices have complex features,such as high frequency,multiple seasonality,and nonlinearity.These factors will make the prediction of electricity prices difficult.However,accurate electricity price prediction is important for energy producers and consumers to develop bidding strategies.To improve the accuracy of prediction by using each algorithms’advantages,this paper proposes a hybrid model that uses the Empirical Mode Decomposition(EMD),Autoregressive Integrated Moving Average(ARIMA),and Temporal Convolutional Network(TCN).EMD is used to decompose the electricity prices into low and high frequency components.Low frequency components are forecasted by the ARIMA model and the high frequency series are predicted by the TCN model.Experimental results using the realistic electricity price data from Pennsylvania-New Jersey-Maryland(PJM)electricity markets show that the proposed method has a higher prediction accuracy than other single methods and hybrid methods.展开更多
This paper presents a control strategy for residential battery energy storage systems,which is aware of volatile electricity markets and uncertain daily cycling loads.The economic benefits of energy trading for prosum...This paper presents a control strategy for residential battery energy storage systems,which is aware of volatile electricity markets and uncertain daily cycling loads.The economic benefits of energy trading for prosumers are achieved through a novel modification of a conventional model predictive control(MPC).The proposed control strategy guarantees an optimal global solution for the applied control action.A new cost function is introduced to model the effects of volatility on customer benefits more effectively.Specifically,the newly presented cost function models a probabilistic relation between the power exchanged with the grid,the net load,and the electricity market.The probabilistic calculation of the cost function shows the dependence on the mathematical expectation of market price and net load.Computational techniques for calculating this value are presented.The proposed strategy differs from the stochastic and robust MPC in that the cost is calculated across the market price and net load variations rather than across model constraints and parameter variations.展开更多
One of the key research problems in financial markets is the investigation of inter-stock dependence.A good understanding in this regard is crucial for portfolio optimization.To this end,various econometric models hav...One of the key research problems in financial markets is the investigation of inter-stock dependence.A good understanding in this regard is crucial for portfolio optimization.To this end,various econometric models have been proposed.Most of them assume that the random noise associated with each subject is independent.However,dependence might still exist within this random noise.Ignoring this valuable information might lead to biased estimations and inaccurate predictions.In this article,we study a spatial autoregressive moving average model with exogenous covariates.Spatial dependence from both response and random noise is considered simultaneously.A quasi-maximum likelihood estimator is developed,and the estimated parameters are shown to be consistent and asymptotically normal.We then conduct an extensive analysis of the proposed method by applying it to the Chinese stock market data.展开更多
The single-input single-output(SISO)j-step-ahead predictor for generalized predictive control(GPC)controllers was traditionally derived using the polynomial approach through the Diophantine equations.An equivalent ver...The single-input single-output(SISO)j-step-ahead predictor for generalized predictive control(GPC)controllers was traditionally derived using the polynomial approach through the Diophantine equations.An equivalent version of the predictor in a state-space form is available in the literature.In this paper,a z-domain analysis of the multiple input multiple output(MIMO)extension of the state-space predictor is carried out,and then an MIMO j-step-ahead predictor in polynomial form based on the controlled auto-regressive moving average model is derived.The predictor enables us to simplify the GPC algorithm design for multivariable systems.In the SISO case the predictor is just the traditional GPC predictor,therefore this paper gives rigorous proof of the equivalence between the traditional GPC predictor and the state-space predictor.展开更多
文摘The energy sector is the second largest emitter of greenhouse (GHG) gases in Kenya, emitting about 31.2% of GHG emissions in the country. The aim of this study was to model Kenya’s GHG emissions by the energy sector using ARIMA models for forecasting future values. The data used for the study was that of Kenya’s GHG emissions by the energy sector for the period starting from 1970 to 2022 obtained for the International Monetary Fund (IMF) database that was split into training and testing sets using the 80/20 rule for modelling purposes. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE). ARIMA (1, 1, 1) was identified as the best model for modelling Kenya’s GHG emissions and forecasting future values. Using this model, Kenya’s GHG emissions by the energy sector were forecasted to increase to a value of about 43.13 million metric tons of carbon dioxide equivalents by 2030. The study, therefore, recommends that Kenya should accelerate the adjustment of industry structure and improve the efficient use of energy, optimize the energy structure and accelerate development and promotion of energy-efficient products to reduce the emission of GHGs by the country’s energy sector.
基金The National Key Research and Development Program of China under contract No.2017YFC1404000the Basic Scientific Fund for National Public Research Institutes of China under contract No.2018S03the National Natural Science Foundation of China under contract Nos 41776038 and 41821004
文摘Oceanic turbulence measurements made by an acoustic Doppler velocimeter(ADV)suffer from noise that potentially affects the estimates of turbulence statistics.This study examines the abilities of Kalman filtering and autoregressive moving average models to eliminate noise in ADV velocity datasets of laboratory experiments and offshore observations.Results show that the two methods have similar performance in ADV de-noising,and both effectively reduce noise in ADV velocities,even in cases of high noise.They eliminate the noise floor at high frequencies of the velocity spectra,leading to a longer range that effectively fits the Kolmogorov-5/3 slope at midrange frequencies.After de-noising adopting the two methods,the values of the mean velocity are almost unchanged,while the root-mean-square horizontal velocities and thus turbulent kinetic energy decrease appreciably in these experiments.The Reynolds stress is also affected by high noise levels,and de-noising thus reduces uncertainties in estimating the Reynolds stress.
基金supported by the Natural Science Foundation of Hunan Province(2023JJ30817)Hunan Provincial Natural Science Foundation-Hengyang City Joint Fund Project(2025JJ70129)+1 种基金Changsha Natural Science Foundation(kq2403057)China。
文摘Objective:Near vision loss(NVL)is one of the leading causes of visual impairment worldwide,exerting a profound impact on individual quality of life and socio-economic development.This study aims to analyze the burden of NVL in China by sex and age groups from 1990 to 2021 and to project trends over the next 15 years.Methods:Using data from the Global Burden of Disease(GBD)2021 database,we conducted descriptive analyses of NVL prevalence in China,calculated age-standardized prevalence rates(ASPR)and age-standardized disability-adjusted life years rates(ASDR)to compare burden differences between sexes and age groups,and applied an autoregressive integrated moving average(ARIMA)model to predict NVL trends for the next 15 years.The model selection was based on best-fit criteria to ensure reliable projections.Results:From 1990 to 2021,China’s ASPR of NVL rose from 10096.24/100000 to 15624.54/100000,and ASDR increased from 101.75/100000 to 158.75/100000.In 2021,ASPR(16551.70/100000)and ASDR(167.69/100000)were higher among females than males(14686.21/100000 and 149.76/100000,respectively).China ranked highest globally in both NVL cases and disability-adjusted life years(DALYs),with female burden significantly exceeding male burden.Projections indicated this trend and sex gap will persist until 2036.Compared with 1990,the prevalence cases and DALYs increased by 239.20%and 238.82%,respectively in 2021,with the highest burden among females and the 55−59 age group.The ARIMA model predicted continued increases in prevalence and DALYs by 2036,with females maintaining a higher burden than males.Conclusion:This study reveals a marked increase in the NVL burden in China and predicts continued growth in the coming years.Public health policies should prioritize NVL prevention and control,with special attention to women and middle-aged populations to mitigate long-term societal and health impacts.
基金supported by the National Natural Science Foundation of China(42174020,42304040)the Fundamental Research Funds for the Central Universities(24CX06045A).
文摘This paper presents a method combining B-splines and autoregressive moving averages for the joint correction of sound velocity disturbances,addressing the problem of existing parameterized models for sound velocity errors in underwater geodetic positioning without considering the temporal correlation of sound velocity disturbances.Initially,a quadratic polynomial and a cubic B-spline model are utilized to preliminarily correct the disturbed sound speed structure.Subsequently,considering temporal correlation variations,sound speed error processing is conducted based on the autoregressive moving average model.Finally,the method is validated using data from the 3000-meter sea trial in the South China Sea.The results indicate that,compared to the quadratic polynomial and cubic B-spline sound speed corrections,applying the autoregressive moving average model for successive sound speed correction reduces the root mean square error of time observation value residuals by 58%and 30%,respectively.
基金The National Natural Science Foundation of China(No.61273236)the Natural Science Foundation of Jiangsu Province(No.BK2010239)the Ph.D.Programs Foundation of Ministry of Education of China(No.200802861061)
文摘This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.
基金Science and Technology Project of State Grid Corporation of China(No.SGGSKY00FJJS1800140)。
文摘For the low utilization rate of photovoltaic power generation,taking a new energy power system constisting of concentrating solar power(CSP),photovoltaic power(PP)and battery energy storage system as an example,a multi-objective optimization scheduling strategy considering energy storage participation is proposed.Firstly,the new energy power system model is established,and the PP scenario generation and reduction frame based on the autoregressive moving average model and Kantorovich-distance is proposed.Then,based on the optimization goal of the system operation cost minimization and the PP output power consumption maximization,the multi-objective optimization scheduling model is established.Finally,the simulation results show that introducing energy storage into the system can effectively reduce the system operation cost and improve the utilization efficiency of PP.
基金Under the auspices of National Natural Science Foundation of China(No.42071230)。
文摘China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity.The country has entered a new development stage designed to achieve its second centenary goal.However,ecological fragility and risk susceptibility have increased the risk of returning to ecological poverty.In this paper,the Liupan Mountain Region of China was used as a case study,and the counties were used as the scale to reveal the spatiotempora differentiation and influcing factors of the risk of returning to poverty in study area.The indicator data for returning to ecological poverty from 2011-2020 were collected and summarized in three dimensions:ecological,economic and social.The autoregressive integrated moving average model(ARIMA)time series and exponential smoothing method(ES)were used to predict the multidimensional indicators of returning to ecological poverty for 61 counties(districts)in the Liupan Mountain Region for 2021-2030.The back propagation neural network(BPNN)and geographic information system(GIS)were used to generate the spatial distribution and time variation for the index of the risk of returning to ecological poverty(RREP index).The results show that 1)ecological factors were the main factors in the risk of returning to ecological poverty in Liupan Mountain Region.2)The RREP index for the 61 counties(districts)exhibited a downward trend from 2021-2030.The RREP index declined more in medium-and high-risk areas than in low-risk areas.From 2021 to 2025,the RREP index exhibited a slight downward trend.From 2026 to2030,the RREP index was expected to decline faster,especially from 2029-2030.3)Based on the RREP index,it can be roughly divided into three types,namely,the high-risk areas,the medium-risk areas,and the low-risk areas.The natural resource conditions in lowrisk areas of returning to ecological poverty,were better than those in medium-and high-risk areas.
文摘Time series analysis has two goals, modeling random mechanisms and predicting future series using historical data. In the present work, a uni-variate time series autoregressive integrated moving average （ARIMA） model has been developed for （a） simulating and forecasting mean rainfall, obtained using Theissen weights; over the Mahanadi River Basin in India, and （b） simula^ag and forecasting mean rainfall at 38 rain-gauge stations in district towns across the basin. For the analysis, monthly rainfall data of each district town for the years 1901-2002 （102 years） were used. Theissen weights were obtained over the basin and mean monthly rainfall was estimated. The trend and seasonality observed in ACF and PACF plots of rainfall data were removed using power transformation （a=0.5） and first order seasonal differencing prior to the development of the ARIMA model. Interestingly, the AR1MA model （1,0,0）（0,1,1）12 developed here was found to be most suitable for simulating and forecasting mean rainfall over the Mahanadi River Basin and for all 38 district town rain-gauge stations, separately. The Akaike Information Criterion （AIC）, good- ness of fit （Chi-square）, R2 （coefficient of determination）, MSE （mean square error） and MAE （mea absolute error） were used to test the validity and applicability of the developed ARIMA model at different stages. This model is considered appropriate to forecast the monthly rainfall for the upcoming 12 years in each district town to assist decision makers and policy makers establish priorities for water demand, storage, distribution, and disaster management.
基金financially supported by the 973 Project (Grant No. 2011CB013704)by the National Natural Science Foundation of China (Grant Nos. 51379005, 51009093)
文摘Based on dynamic response signals a damage detection algorithm is developed for marine risers. Damage detection methods based on numerous modal properties have encountered issues in the researches in offshore oil community. For example, significant increase in structure mass due to marine plant/animal growth and changes in modal properties by equipment noise are not the result of damage for riser structures. In an attempt to eliminate the need to determine modal parameters, a data-based method is developed. The implementation of the method requires that vibration data are first standardized to remove the influence of different loading conditions and the autoregressive moving average(ARMA) model is used to fit vibration response signals. In addition, a damage feature factor is introduced based on the autoregressive(AR) parameters. After that, the Euclidean distance between ARMA models is subtracted as a damage indicator for damage detection and localization and a top tensioned riser simulation model with different damage scenarios is analyzed using the proposed method with dynamic acceleration responses of a marine riser as sensor data. Finally, the influence of measured noise is analyzed. According to the damage localization results, the proposed method provides accurate damage locations of risers and is robust to overcome noise effect.
基金supported by the National High-Tech Research and Development Plan of China under Grant No. 2006AA01Z448 (863)the Key Science and Technology Research project of Ministry of Education of China under Grant No. 108013+1 种基金the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant No. 60821001the National Information Security Plan of China under Grant No.2007A14 (242)
文摘Existing detection methods against SYN flooding attacks are effective only at the later stages when attacking signatures are obvious.In this paper an early stage detecting method(ESDM) is proposed.The ESDM is a simple but effective method to detect SYN flooding attacks at the early stage.In the ESDM the SYN traffic is forecasted by autoregressive integrated moving average model, and non-parametric cumulative sum algorithm is used to find the SYN flooding attacks according to the forecasted traffic.Trace-driven simulations show that ESDM is accurate and efficient to detect the SYN flooding attacks.
基金This paper is partially supported by the basic scientific research business expenses of Universities in Xinjiang,China[Grant Number XQZX20230057]the National Natural Science Foundation of China[Grant Number 11671142].
文摘In the paper,the autoregressive moving average model for matrix time series(MARMA)is inves-tigated.The properties of the MARMA model are investigated by using the conditional least square estimation,the conditional maximum likelihood estimation,the projection theorem in Hilbert space and the decomposition technique of time series,which include necessary and suf-ficient conditions for stationarity and invertibility,model parameter estimation,model testing and model forecasting.
文摘The stock market is a vital component of the broader financial system,with its dynamics closely linked to economic growth.The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets.By examining historical transaction data,latent opportunities for profit can be uncovered,providing valuable insights for both institutional and individual investors to make more informed decisions.This study focuses on analyzing historical transaction data from four banks to predict closing price trends.Various models,including decision trees,random forests,and Long Short-Term Memory(LSTM)networks,are employed to forecast stock price movements.Historical stock transaction data serves as the input for training these models,which are then used to predict upward or downward stock price trends.The study’s empirical results indicate that these methods are effective to a degree in predicting stock price movements.The LSTM-based deep neural network model,in particular,demonstrates a commendable level of predictive accuracy.This conclusion is reached following a thorough evaluation of model performance,highlighting the potential of LSTM models in stock market forecasting.The findings offer significant implications for advancing financial forecasting approaches,thereby improving the decision-making capabilities of investors and financial institutions.
基金Project supported by the National Science &Technology Pillar Program(No.2014BAG01B02)
文摘The realization of road traffic prediction not only provides real-time and effective information for travelers, but also helps them select the optimal route to reduce travel time. Road traffic prediction offers traffic guidance for travelers and relieves traffic jams. In this paper, a real-time road traffic state prediction based on autoregressive integrated moving average （ARIMA） and the Kalman filter is proposed. First, an ARIMA model of road traffic data in a time series is built on the basis of historical road traffic data. Second, this ARIMA model is combined with the Kalman filter to construct a road traffic state prediction algorithm, which can acquire the state, measurement, and updating equations of the Kalman filter. Third, the optimal parameters of the algorithm are discussed on the basis of historical road traffic data. Finally, four road segments in Beijing are adopted for case studies. Experimental results show that the real-time road traffic state prediction based on ARIMA and the Kalman filter is feasible and can achieve high accuracy.
基金supported by the National Natural ScienceFoundation of China(Grant No.41672360)Science and Technology Commission of Shanghai Munici-pality(Grant No.17DZ1203800)Shanghai Shentong Metro Group Co.,Ltd.(Grant No.17DZ1203804).
文摘Proper regulation of the earth pressure on the bulkhead of earth-pressure balanced(EPB)shield tunneling machines is significant to ensure safe construction.This study proposes a procedure for regulating the bulkhead pressure by combining numerical simulations and data mining,and applies the procedure to a metro line constructed in sandy pebble stratum using EPB shield.Firstly,the relationship between the bulkhead pressure and the pressure on the tunnel face is carefully obtained from discrete element modeling,and the required supporting earth pressure is derived by considering the arching effect.Secondly,aided with the machine learning method,a model is constructed for predicting the average bulkhead pressure per ring according to the operational parameters(i.e.,the average driving speed and the rotation speed of the screw conveyor).Given the target value of the bulkhead pressure,the optimal values of the operational parameters are obtained from the model.In addition,an autoregressive moving average stochastic process model is developed to monitor the real-time fluctuation of the bulkhead pressure and guide the actions taken in time to avoid dramatic fluctuations.The results indicate that the pressure difference between the tunnel face and the bulkhead is considerable,and the consideration of the arching effect can avoid overestimating the bulkhead pressure.A combination of the machine learning model and the stochastic process model provides a plausible performance in regulating the bulkhead pressure around the target value without dramatic fluctuation.
基金supported by the Sichuan Science and Technology Program under Grant 2020JDJQ0037 and 2020YFG0312.
文摘Electricity prices have complex features,such as high frequency,multiple seasonality,and nonlinearity.These factors will make the prediction of electricity prices difficult.However,accurate electricity price prediction is important for energy producers and consumers to develop bidding strategies.To improve the accuracy of prediction by using each algorithms’advantages,this paper proposes a hybrid model that uses the Empirical Mode Decomposition(EMD),Autoregressive Integrated Moving Average(ARIMA),and Temporal Convolutional Network(TCN).EMD is used to decompose the electricity prices into low and high frequency components.Low frequency components are forecasted by the ARIMA model and the high frequency series are predicted by the TCN model.Experimental results using the realistic electricity price data from Pennsylvania-New Jersey-Maryland(PJM)electricity markets show that the proposed method has a higher prediction accuracy than other single methods and hybrid methods.
基金supported by Australian Research Council (ARC)Discovery Project (No.160102571)。
文摘This paper presents a control strategy for residential battery energy storage systems,which is aware of volatile electricity markets and uncertain daily cycling loads.The economic benefits of energy trading for prosumers are achieved through a novel modification of a conventional model predictive control(MPC).The proposed control strategy guarantees an optimal global solution for the applied control action.A new cost function is introduced to model the effects of volatility on customer benefits more effectively.Specifically,the newly presented cost function models a probabilistic relation between the power exchanged with the grid,the net load,and the electricity market.The probabilistic calculation of the cost function shows the dependence on the mathematical expectation of market price and net load.Computational techniques for calculating this value are presented.The proposed strategy differs from the stochastic and robust MPC in that the cost is calculated across the market price and net load variations rather than across model constraints and parameter variations.
基金supported by the Major Program of the National Natural Science Foundation of China (Grant No. 11731101)National Natural Science Foundation of China (Grant No. 11671349)+6 种基金supported by National Natural Science Foundation of China (Grant No. 72171226)the Beijing Municipal Social Science Foundation (Grant No. 19GLC052)the National Statistical Science Research Project (Grant No. 2020LZ38)supported by National Natural Science Foundation of China (Grant Nos. 71532001, 11931014, 12171395 and 71991472)the Joint Lab of Data Science and Business Intelligence at Southwestern University of Finance and Economicssupported by National Natural Science Foundation of China (Grant No. 11831008)the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science (Grant No. Klatasds-Moe-EcnuKlatasds2101)
文摘One of the key research problems in financial markets is the investigation of inter-stock dependence.A good understanding in this regard is crucial for portfolio optimization.To this end,various econometric models have been proposed.Most of them assume that the random noise associated with each subject is independent.However,dependence might still exist within this random noise.Ignoring this valuable information might lead to biased estimations and inaccurate predictions.In this article,we study a spatial autoregressive moving average model with exogenous covariates.Spatial dependence from both response and random noise is considered simultaneously.A quasi-maximum likelihood estimator is developed,and the estimated parameters are shown to be consistent and asymptotically normal.We then conduct an extensive analysis of the proposed method by applying it to the Chinese stock market data.
基金supported by the National Natural Science Foundation of China(Grant No.60404010).
文摘The single-input single-output(SISO)j-step-ahead predictor for generalized predictive control(GPC)controllers was traditionally derived using the polynomial approach through the Diophantine equations.An equivalent version of the predictor in a state-space form is available in the literature.In this paper,a z-domain analysis of the multiple input multiple output(MIMO)extension of the state-space predictor is carried out,and then an MIMO j-step-ahead predictor in polynomial form based on the controlled auto-regressive moving average model is derived.The predictor enables us to simplify the GPC algorithm design for multivariable systems.In the SISO case the predictor is just the traditional GPC predictor,therefore this paper gives rigorous proof of the equivalence between the traditional GPC predictor and the state-space predictor.
