Time series forecasting has become an important aspect of data analysis and has many real-world applications.However,undesirable missing values are often encountered,which may adversely affect many forecasting tasks.I...Time series forecasting has become an important aspect of data analysis and has many real-world applications.However,undesirable missing values are often encountered,which may adversely affect many forecasting tasks.In this study,we evaluate and compare the effects of imputationmethods for estimating missing values in a time series.Our approach does not include a simulation to generate pseudo-missing data,but instead perform imputation on actual missing data and measure the performance of the forecasting model created therefrom.In an experiment,therefore,several time series forecasting models are trained using different training datasets prepared using each imputation method.Subsequently,the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting models.The results obtained from a total of four experimental cases show that the k-nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods.展开更多
This paper discusses the modeling method of time series with neural network. In order to improve the adaptability of direct multi-step prediction models, this paper proposes a method of combining the temporal differen...This paper discusses the modeling method of time series with neural network. In order to improve the adaptability of direct multi-step prediction models, this paper proposes a method of combining the temporal differences methods with back-propagation algorithm for updating the parameters continuously on the basis of recent data. This method can make the neural network model fit the recent characteristic of the time series as close as possible, therefore improves the prediction accuracy. We built models and made predictions for the sunspot series. The prediction results of adaptive modeling method are better than that of non-adaptive modeling methods.展开更多
In this paper a new .mnultidimensional time series forecasting scheme based on the empirical orthogonal function (EOF) stepwise iteration process is introduced. The scheme is tested in a series of forecast experiments...In this paper a new .mnultidimensional time series forecasting scheme based on the empirical orthogonal function (EOF) stepwise iteration process is introduced. The scheme is tested in a series of forecast experiments of Nino3 SST anomalies and Tahiti-Darwin SO index. The results show that the scheme is feasible and ENSO predictable.展开更多
This paper depends on the panel data of Anhui province and its 17 cities’ cigarette sales. First we established three single forecasting models (Holter-Wintel Season product model, Time series model decomposing model...This paper depends on the panel data of Anhui province and its 17 cities’ cigarette sales. First we established three single forecasting models (Holter-Wintel Season product model, Time series model decomposing model and Partial least square regression model), after getting the predicted value of cigarette sales from these single models, we then employ the combination forecasting method based on Time Series method and PLS to predict the province and its 17 cities’ cigarette sales of the next year. The results show that the accuracy of prediction is good which could provide a reliable reference to cigarette sales forecasting in Anhui province and its 17 cities.展开更多
Cigarette market is a kind of monopoly market which is closed loop running, it depends on the plan mechanism to schedule producing, supplying and selling, but the “bullwhip effect” still exists. So it has a fundamen...Cigarette market is a kind of monopoly market which is closed loop running, it depends on the plan mechanism to schedule producing, supplying and selling, but the “bullwhip effect” still exists. So it has a fundamental significance to do sales forecasting work. It needs to considerate the double trend characteristics, history sales data and other main factors that affect cigarette sales. This paper depends on the panel data of A province’s cigarette sales, first we established three single forecasting models, after getting the predicted value of these single models, then using the combination forecasting method which based on PLS to predict the province’s cigarette sales of the next year. The results show that the prediction accuracy is good, which could provide a certain reference to cigarette sales forecasting in A province.展开更多
Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory p...Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory performance. This paper proposes a forecast method： Genetic programming based on least square method （GP-LSM）. Inheriting the advantages of genetic algorithm （GA）, without relying on the particular distribution of the data, this method can improve the prediction accuracy because of its ability of fitting nonlinear models, and raise the convergence speed benefitting from the least square method （LSM）. In order to verify the vMidity of this method, the authors compare this method with seasonal auto regression integrated moving average （SARIMA） and back propagation artificial neural networks （BP-ANN）. The results of empirical analysis show that forecast accuracy and direction prediction accuracy of GP-LSM are obviously better than those of the others.展开更多
This study proposes two metrics using the nearest neighbors method to improve the accuracy of time-series forecasting. These two metrics can be treated as a hybrid forecasting approach to combine linear and non-linear...This study proposes two metrics using the nearest neighbors method to improve the accuracy of time-series forecasting. These two metrics can be treated as a hybrid forecasting approach to combine linear and non-linear forecasting techniques. One metric redefines the distance in k-nearest neighbors based on the coefficients of autoregression (AR) in time series. Meanwhile, an improvement to Kulesh's adaptive metrics in the nearest neighbors is also presented. To evaluate the performance of the two proposed metrics, three types of time-series data, namely deterministic synthetic data, chaotic time-series data and real time-series data, are predicted. Experimental results show the superiority of the proposed AR-enhanced k-nearest neighbors methods to the traditional k-nearest neighbors metric and Kulesh's adaptive metrics.展开更多
In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using concept...In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.展开更多
Hierarchical time series arise in manufacturing and service industries when the products or services have the hierarchical structure,and top-down and bottomup methods are commonly used to forecast the hierarchical tim...Hierarchical time series arise in manufacturing and service industries when the products or services have the hierarchical structure,and top-down and bottomup methods are commonly used to forecast the hierarchical time series.One of the critical factors that affect the performance of the two methods is the correlation between the data series.This study attempts to resolve the problem and shows that the top-down method performs better when data have high positive correlation compared to high negative correlation and combination of forecasting methods may be the best solution when there is no evidence of the correlationship.We conduct the computational experiments using 240 monthly data series from the‘Industrial’category of the M3-Competition and test twelve combination methods for the hierarchical data series.The results show that the regression-based,VAR-COV and the Rank-based methods perform better compared to the other methods.展开更多
Accurately forecasting the nonlinear degradation of lithium-ion batteries(LIBs)using early-cycle data can obviously shorten the battery test time,which accelerates battery optimization and production.In this work,a se...Accurately forecasting the nonlinear degradation of lithium-ion batteries(LIBs)using early-cycle data can obviously shorten the battery test time,which accelerates battery optimization and production.In this work,a self-adaptive long short-term memory(SA-LSTM)method has been proposed to predict the battery degradation trajectory and battery lifespan with only early cycling data.Specifically,two features were extracted from discharge voltage curves by a time-series-based approach and forecasted to further cycles using SA-LSTM model.The as-obtained features were correlated with the capacity to predict the capacity degradation trajectory by generalized multiple linear regression model.The proposed method achieved an average online prediction error of 6.00%and 6.74%for discharge capacity and end of life,respectively,when using the early-cycle discharge information until 90%capacity retention.Fur-thermore,the importance of temperature control was highlighted by correlat-ing the features with the average temperature in each cycle.This work develops a self-adaptive data-driven method to accurately predict the cycling life of LIBs,and unveils the underlying degradation mechanism and the impor-tance of controlling environmental temperature.展开更多
Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy,the comprehending-intrinsic complexity of the behavior of wind is considered as the main challenge faced in improvin...Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy,the comprehending-intrinsic complexity of the behavior of wind is considered as the main challenge faced in improving forecasting accuracy.To improve forecasting accuracy,this paper focuses on two aspects:①proposing a novel hybrid method using Boosting algorithm and a multistep forecast approach to improve the forecasting capacity of traditional ARMA model;②calculating the existing error bounds of the proposed method.To validate the effectiveness of the novel hybrid method,one-year period of real data are used for test,which were collected from three operating wind farms in the east coast of Jiangsu Province,China.Meanwhile conventional ARMA model and persistence model are both used as benchmarks with which the proposed method is compared.Test results show that the proposed method achieves a more accurate forecast.展开更多
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(Grant Number 2020R1A6A1A03040583).
文摘Time series forecasting has become an important aspect of data analysis and has many real-world applications.However,undesirable missing values are often encountered,which may adversely affect many forecasting tasks.In this study,we evaluate and compare the effects of imputationmethods for estimating missing values in a time series.Our approach does not include a simulation to generate pseudo-missing data,but instead perform imputation on actual missing data and measure the performance of the forecasting model created therefrom.In an experiment,therefore,several time series forecasting models are trained using different training datasets prepared using each imputation method.Subsequently,the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting models.The results obtained from a total of four experimental cases show that the k-nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods.
文摘This paper discusses the modeling method of time series with neural network. In order to improve the adaptability of direct multi-step prediction models, this paper proposes a method of combining the temporal differences methods with back-propagation algorithm for updating the parameters continuously on the basis of recent data. This method can make the neural network model fit the recent characteristic of the time series as close as possible, therefore improves the prediction accuracy. We built models and made predictions for the sunspot series. The prediction results of adaptive modeling method are better than that of non-adaptive modeling methods.
文摘In this paper a new .mnultidimensional time series forecasting scheme based on the empirical orthogonal function (EOF) stepwise iteration process is introduced. The scheme is tested in a series of forecast experiments of Nino3 SST anomalies and Tahiti-Darwin SO index. The results show that the scheme is feasible and ENSO predictable.
文摘This paper depends on the panel data of Anhui province and its 17 cities’ cigarette sales. First we established three single forecasting models (Holter-Wintel Season product model, Time series model decomposing model and Partial least square regression model), after getting the predicted value of cigarette sales from these single models, we then employ the combination forecasting method based on Time Series method and PLS to predict the province and its 17 cities’ cigarette sales of the next year. The results show that the accuracy of prediction is good which could provide a reliable reference to cigarette sales forecasting in Anhui province and its 17 cities.
文摘Cigarette market is a kind of monopoly market which is closed loop running, it depends on the plan mechanism to schedule producing, supplying and selling, but the “bullwhip effect” still exists. So it has a fundamental significance to do sales forecasting work. It needs to considerate the double trend characteristics, history sales data and other main factors that affect cigarette sales. This paper depends on the panel data of A province’s cigarette sales, first we established three single forecasting models, after getting the predicted value of these single models, then using the combination forecasting method which based on PLS to predict the province’s cigarette sales of the next year. The results show that the prediction accuracy is good, which could provide a certain reference to cigarette sales forecasting in A province.
基金supported by the National Natural Science Foundation of China under Grant Nos.71171011 and 91224001Program for New Century Excellent Talents in University(NCET-12-0756)
文摘Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory performance. This paper proposes a forecast method： Genetic programming based on least square method （GP-LSM）. Inheriting the advantages of genetic algorithm （GA）, without relying on the particular distribution of the data, this method can improve the prediction accuracy because of its ability of fitting nonlinear models, and raise the convergence speed benefitting from the least square method （LSM）. In order to verify the vMidity of this method, the authors compare this method with seasonal auto regression integrated moving average （SARIMA） and back propagation artificial neural networks （BP-ANN）. The results of empirical analysis show that forecast accuracy and direction prediction accuracy of GP-LSM are obviously better than those of the others.
基金the National Natural Science Foundation of China(No.61203337)the Natural Science Foundation of Shanghai(No.12ZR1440200)
文摘This study proposes two metrics using the nearest neighbors method to improve the accuracy of time-series forecasting. These two metrics can be treated as a hybrid forecasting approach to combine linear and non-linear forecasting techniques. One metric redefines the distance in k-nearest neighbors based on the coefficients of autoregression (AR) in time series. Meanwhile, an improvement to Kulesh's adaptive metrics in the nearest neighbors is also presented. To evaluate the performance of the two proposed metrics, three types of time-series data, namely deterministic synthetic data, chaotic time-series data and real time-series data, are predicted. Experimental results show the superiority of the proposed AR-enhanced k-nearest neighbors methods to the traditional k-nearest neighbors metric and Kulesh's adaptive metrics.
基金Project(08SK1002) supported by the Major Project of Science and Technology Department of Hunan Province,China
文摘In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system.
基金This work was supported by Program of Shanghai Subject Chief Scientist[16XD1401700]National Natural Science Foundation of China[71421002].
文摘Hierarchical time series arise in manufacturing and service industries when the products or services have the hierarchical structure,and top-down and bottomup methods are commonly used to forecast the hierarchical time series.One of the critical factors that affect the performance of the two methods is the correlation between the data series.This study attempts to resolve the problem and shows that the top-down method performs better when data have high positive correlation compared to high negative correlation and combination of forecasting methods may be the best solution when there is no evidence of the correlationship.We conduct the computational experiments using 240 monthly data series from the‘Industrial’category of the M3-Competition and test twelve combination methods for the hierarchical data series.The results show that the regression-based,VAR-COV and the Rank-based methods perform better compared to the other methods.
基金supported by the National Key Research and Development Program(2021YFB2500300)Beijing Municipal Natural Science Foundation(Z200011)+1 种基金National Natural Science Foundation of China(T2322015,22209093,22209094,22379121,and 21825501)the Fundamental Research Funds for the Central Universities.
文摘Accurately forecasting the nonlinear degradation of lithium-ion batteries(LIBs)using early-cycle data can obviously shorten the battery test time,which accelerates battery optimization and production.In this work,a self-adaptive long short-term memory(SA-LSTM)method has been proposed to predict the battery degradation trajectory and battery lifespan with only early cycling data.Specifically,two features were extracted from discharge voltage curves by a time-series-based approach and forecasted to further cycles using SA-LSTM model.The as-obtained features were correlated with the capacity to predict the capacity degradation trajectory by generalized multiple linear regression model.The proposed method achieved an average online prediction error of 6.00%and 6.74%for discharge capacity and end of life,respectively,when using the early-cycle discharge information until 90%capacity retention.Fur-thermore,the importance of temperature control was highlighted by correlat-ing the features with the average temperature in each cycle.This work develops a self-adaptive data-driven method to accurately predict the cycling life of LIBs,and unveils the underlying degradation mechanism and the impor-tance of controlling environmental temperature.
基金supported by the National High Technology Research and Development of China (863 Program) (No. 2012AA050214)the National Natural Science Foundation of China (No. 51077043)the State Grid Corporation of China (Impact research of source-grid-load interaction on operation and control of future power system)
文摘Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy,the comprehending-intrinsic complexity of the behavior of wind is considered as the main challenge faced in improving forecasting accuracy.To improve forecasting accuracy,this paper focuses on two aspects:①proposing a novel hybrid method using Boosting algorithm and a multistep forecast approach to improve the forecasting capacity of traditional ARMA model;②calculating the existing error bounds of the proposed method.To validate the effectiveness of the novel hybrid method,one-year period of real data are used for test,which were collected from three operating wind farms in the east coast of Jiangsu Province,China.Meanwhile conventional ARMA model and persistence model are both used as benchmarks with which the proposed method is compared.Test results show that the proposed method achieves a more accurate forecast.
