A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of avail...A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.展开更多
Fuzzy sets theory cannot describe the neutrality degreeof data, which has largely limited the objectivity of fuzzy time seriesin uncertain data forecasting. With this regard, a multi-factor highorderintuitionistic fuz...Fuzzy sets theory cannot describe the neutrality degreeof data, which has largely limited the objectivity of fuzzy time seriesin uncertain data forecasting. With this regard, a multi-factor highorderintuitionistic fuzzy time series forecasting model is built. Inthe new model, a fuzzy clustering algorithm is used to get unequalintervals, and a more objective technique for ascertaining membershipand non-membership functions of the intuitionistic fuzzy setis proposed. On these bases, forecast rules based on multidimensionalintuitionistic fuzzy modus ponens inference are established.Finally, contrast experiments on the daily mean temperature ofBeijing are carried out, which show that the novel model has aclear advantage of improving the forecast accuracy.展开更多
Ever since the impoundment of Three Gorges Reservoir（TGR）, the seismicity in head region of TGR has increased significantly. Coupled with wide fluctuation of water level each year, it becomes more important to study...Ever since the impoundment of Three Gorges Reservoir（TGR）, the seismicity in head region of TGR has increased significantly. Coupled with wide fluctuation of water level each year, it becomes more important to study the deformation forecasting of landslides beside TGR. As a famous active landslide beside TGR, Huangtupo riverside landslide is selected for a case study. Based on long term water level fluctuation and seismic monitoring, three typical adverse conditions are determined. With the established 3D numerical landslide model, seepage-dynamic coupling calculation is conducted under the seismic intensity of V degree. Results are as follows： 1. the dynamic water pressure formed by water level fluctuation will intensify the deformation of landslide; 2. under seismic load, the dynamic hysteresis is significant in defective geological bodies, such as weak layer and slip zone soil, because of much higher damping ratios, the seismic accelerate would be amplified in these elements; 3. microseisms are not intense enough to cause the landslide instability suddenly, but long term deformation accumulation effect of landslide should be paid more attention; 4. in numerical simulation, the factors of unbalance force and excess pore pressure also can be used in forecasting deformation tendency of landslide.展开更多
Time series data plays a crucial role in intelligent transportation systems.Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval.Existing approache...Time series data plays a crucial role in intelligent transportation systems.Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval.Existing approaches,including sequence periodic,regression,and deep learning models,have shown promising results in short-term series forecasting.However,forecasting scenarios specifically focused on holiday traffic flow present unique challenges,such as distinct traffic patterns during vacations and the increased demand for long-term forecastings.Consequently,the effectiveness of existing methods diminishes in such scenarios.Therefore,we propose a novel longterm forecasting model based on scene matching and embedding fusion representation to forecast long-term holiday traffic flow.Our model comprises three components:the similar scene matching module,responsible for extracting Similar Scene Features;the long-short term representation fusion module,which integrates scenario embeddings;and a simple fully connected layer at the head for making the final forecasting.Experimental results on real datasets demonstrate that our model outperforms other methods,particularly in medium and long-term forecasting scenarios.展开更多
Urban grid power forecasting is one of the important tasks of power system operators, which helps to analyze the development trend of the city. As the demand for electricity in various industries is affected by many f...Urban grid power forecasting is one of the important tasks of power system operators, which helps to analyze the development trend of the city. As the demand for electricity in various industries is affected by many factors, the data of relevant influencing factors are scarce, resulting in great deviations in the accuracy of prediction results. In order to improve the prediction results, this paper proposes a model based on Multi-Target Tree Regression to predict the monthly electricity consumption of different industrial structures. Due to few data characteristics of actual electricity consumption in Shanghai from 2013 to the first half of 2017. Thus, we collect data on GDP growth, weather conditions, and tourism season distribution in various industries in Shanghai, model and train the electricity consumption data of different industries in different months. The multi-target tree regression model was tested with actual values to verify the reliability of the model and predict the monthly electricity consumption of each industry in the second half of 2017. The experimental results show that the model can accurately predict the monthly electricity consumption of various industries.展开更多
In semiconductor and electronics factories, large multi-chiller systems are needed to satisfy strict cooling load requirements. In order to save energy, it is worthwhile to design the chilled water system operation. I...In semiconductor and electronics factories, large multi-chiller systems are needed to satisfy strict cooling load requirements. In order to save energy, it is worthwhile to design the chilled water system operation. In this paper, an optimal flexible operation scheme is developed based on a two-dimensional time-series model to forecast the cooling load of multi-chiller systems with chiller units of different cooling capacities running in parallel. The optimal integrity scheme can be obtained using the Mixed Integer Nonlinear Programming method, which minimizes the energy consumption of the system within a future time period. In order to better adapt the change of cooling load, the operation strategy of regulating the chilled water flowrates is employed. The chilled water flowrates are set as a design variable. When the chillers are running, their chilled water flowrates can vary within limits, whereas the flowrates are zero when the chillers are unloaded. This forecasting method provides integral optimization within a future time period and offers the operating reference for operators. The power and advantages of the proposed method are presented using an industrial case to help readers delve into this matter.展开更多
<div style="text-align:justify;"> <span style="font-family:Verdana;">Sensitivity analysis of neural networks to input variation is an important research area as it goes some way to addr...<div style="text-align:justify;"> <span style="font-family:Verdana;">Sensitivity analysis of neural networks to input variation is an important research area as it goes some way to addressing the criticisms of their black-box behaviour. Such analysis of RBFNs for hydrological modelling has previously been limited to exploring perturbations to both inputs and connecting weights. In this paper, the backward chaining rule that has been used for sensitivity analysis of MLPs, is applied to RBFNs and it is shown how such analysis can provide insight into physical relationships. A trigonometric example is first presented to show the effectiveness and accuracy of this approach for first order derivatives alongside a comparison of the results with an equivalent MLP. The paper presents a real-world application in the modelling of river stage shows the importance of such approaches helping to justify and select such models.</span> </div>展开更多
径流过程呈现出的强非线性,使得现有水文模型的预测性能受到制约,深度学习等人工智能方法具有较强的非线性拟合能力,一定程度上可以突破现有瓶颈。为有效提取径流序列的非线性时变特征信息,提高径流模拟精度和多步预测性能,以雅砻江上...径流过程呈现出的强非线性,使得现有水文模型的预测性能受到制约,深度学习等人工智能方法具有较强的非线性拟合能力,一定程度上可以突破现有瓶颈。为有效提取径流序列的非线性时变特征信息,提高径流模拟精度和多步预测性能,以雅砻江上游雅江流域为研究对象,建立了基于具有时变结构的ForecastNet径流预测模型,并与传统水文模型SWAT(Soil and Water Assessnent Teol)和神经网络模型RNN(Recurrent Neural Network)、LSTM(Long Short-Term Memory)及其组合进行对比分析。结果表明,ForcastNet模型在长预见期径流预测中有较强的适用性,能有效提高径流模拟及多步预测精度,为高精度实时径流预测提供了一种技术支撑。展开更多
The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in ni...The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient （ACC） has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble （MME） forecast.展开更多
This paper aims to study a novel expansion discrete grey forecasting model, which could aggregate input information more effectively. In general, existing multi-factor grey forecasting models, such as one order and h ...This paper aims to study a novel expansion discrete grey forecasting model, which could aggregate input information more effectively. In general, existing multi-factor grey forecasting models, such as one order and h variables grey forecasting model （GM （1, h））, always aggregate the main system variable and independent variables in a linear form rather than a nonlinear form, while a nonlinear form could be used in more cases than the linear form. And the nonlinear form could aggregate collinear independent factors, which widely lie in many multi-factor forecasting problems. To overcome this problem, a new approach, named as the Solow residual method, is proposed to aggregate independent factors. And a new expansion model, feedback multi-factor discrete grey forecasting model based on the Solow residual method （abbreviated as FDGM （1, h））, is proposed accordingly. Then the feedback control equation and the parameters＇ solution of the FDGM （1, h） model are given. Finally, a real application is used to test the modelling accuracy of the FDGM （1, h） model. Results show that the FDGM （1, h） model is much better than the nonhomogeneous discrete grey forecasting model （NDGM） and the GM （1, h） model.展开更多
[Objective] The aim was to study the refined forecast method of daily highest temperature in Wugang City from July to September. IM[ethod] By dint of ECMWF mode product and T231 in 2009 and 2010 and daily maximum temp...[Objective] The aim was to study the refined forecast method of daily highest temperature in Wugang City from July to September. IM[ethod] By dint of ECMWF mode product and T231 in 2009 and 2010 and daily maximum temperature in the station in corresponding period, multi-factors similar forecast method to select forecast sample, multivariate regression multi-mode integration MOS method, after dynamic corrected mode error and regression error, dynamic forecast equation was concluded to formulate the daily maximum temperature forecast in 24 -120 h in Wugang City from July to September. [ Result] Through selection, error correction, the daily maximum temperature equation in Wugang City from July to September was concluded. Through multiple random sampling, F test was made to pass test with significant test of 0.1. [ Conclusionl The method integrated domestic and foreign forecast mode, made full use of useful information of many modes, absorbed each others advantages, con- sidered local regional environment, lessen mode and regression error, and improved forecast accuracy.展开更多
The reformation of the economy system has led the f un ctional department and status of the enterprises into a variable state. Under th e condition of the market economy, the kernel of the enterprises’ functional dep...The reformation of the economy system has led the f un ctional department and status of the enterprises into a variable state. Under th e condition of the market economy, the kernel of the enterprises’ functional dep artment has diverted to that of marketing decision-making, which face to market and meet with the need of consumption. Assuredly, the kernel of marketing decis ion-making is to prognosticate the future market demand of the production of en terprises accurately, so that it can ensure and realize the maximum of the enter prises’ profit increase. Using empirical research and the multi-regression technique, this paper ana lyzes the enterprises’ production demand forecast of the GMC (Global Management Challenge, held every year globally) and changes most of uncontrollable factors of demand forecast to the controllable ones of the enterprises. The method we us ed to forecast demand by using the multi-regression technique is as follows: 1. Look for the main factors which influence the demand of productions; 2. Establish the regression model; 3. Using the historical data, find the resolution of the correlative index an d do the prominent test; 4. Analyze and compare, regression, adjust parameter and optimize the regress ion model. Our method will make the forecast data closer to the actual prices of the future market requirement quantity in the production marketing decision-making of the enterprises and realize the optimizing combination and the working object w ith the minimum of the cost and the maximum of the profit. And it can ensure the realization of the equity maximum of the enterprises and increase the lifecycle of the production.展开更多
For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machin...For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machine（LS-SVM） is presented. The multi-agent genetic algorithm（MAGA） is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision,calculation speed and stability.展开更多
The objective of this study is to improve the statistical modeling for the ternary forecast of heavy snowfall in the Honam area in Korea. The ternary forecast of heavy snowfall consists of one of three values, 0 for l...The objective of this study is to improve the statistical modeling for the ternary forecast of heavy snowfall in the Honam area in Korea. The ternary forecast of heavy snowfall consists of one of three values, 0 for less than 50 mm, 1 for an advisory （50-150 ram）, and 2 for a warning （more than 150 mm）. For our study, the observed daily snow amounts and the numerical model outputs for 45 synoptic factors at 17 stations in the Honam area during 5 years （2001 to 2005） are used as observations and potential predictors respectively. For statistical modeling and validation, the data set is divided into training data and validation data by cluster analysis. A multi-grade logistic regression model and neural networks are separately applied to generate the probabilities of three categories based on the model output statistic （MOS） method. Two models are estimated by the training data and tested by the validation data. Based on the estimated probabilities, three thresholds are chosen to generate ternary forecasts. The results are summarized in 3 × 3 contingency tables and the results of the three-grade logistic regression model are compared to those of the neural networks model. According to the model training and model validation results, the estimated three-grade logistic regression model is recommended as a ternary forecast model for heavy snowfall in the Honam area.展开更多
基金Project(51561135003)supported by the International Cooperation and Exchange of the National Natural Science Foundation of ChinaProject(51338003)supported by the Key Project of National Natural Science Foundation of China
文摘A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.
基金supported by the National Natural Science Foundation of China(61309022)
文摘Fuzzy sets theory cannot describe the neutrality degreeof data, which has largely limited the objectivity of fuzzy time seriesin uncertain data forecasting. With this regard, a multi-factor highorderintuitionistic fuzzy time series forecasting model is built. Inthe new model, a fuzzy clustering algorithm is used to get unequalintervals, and a more objective technique for ascertaining membershipand non-membership functions of the intuitionistic fuzzy setis proposed. On these bases, forecast rules based on multidimensionalintuitionistic fuzzy modus ponens inference are established.Finally, contrast experiments on the daily mean temperature ofBeijing are carried out, which show that the novel model has aclear advantage of improving the forecast accuracy.
基金financially supported by the National Natural Science Foundation of China (Nos. 51409011 and 51309029)the Basic Scientific Research Operating Expenses of Central-Level Public Academies and Institutes (Nos. CKSF2014057/YT and CKSF2015051/YT)
文摘Ever since the impoundment of Three Gorges Reservoir（TGR）, the seismicity in head region of TGR has increased significantly. Coupled with wide fluctuation of water level each year, it becomes more important to study the deformation forecasting of landslides beside TGR. As a famous active landslide beside TGR, Huangtupo riverside landslide is selected for a case study. Based on long term water level fluctuation and seismic monitoring, three typical adverse conditions are determined. With the established 3D numerical landslide model, seepage-dynamic coupling calculation is conducted under the seismic intensity of V degree. Results are as follows： 1. the dynamic water pressure formed by water level fluctuation will intensify the deformation of landslide; 2. under seismic load, the dynamic hysteresis is significant in defective geological bodies, such as weak layer and slip zone soil, because of much higher damping ratios, the seismic accelerate would be amplified in these elements; 3. microseisms are not intense enough to cause the landslide instability suddenly, but long term deformation accumulation effect of landslide should be paid more attention; 4. in numerical simulation, the factors of unbalance force and excess pore pressure also can be used in forecasting deformation tendency of landslide.
基金funded by the Natural Science Foundation of Zhejiang Province of China under Grant (No.LY21F020003)Zhejiang Science and Technology Plan Project (No.2021C02060)the Scientific Research Foundation of Hangzhou City University (No.X-202206).
文摘Time series data plays a crucial role in intelligent transportation systems.Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval.Existing approaches,including sequence periodic,regression,and deep learning models,have shown promising results in short-term series forecasting.However,forecasting scenarios specifically focused on holiday traffic flow present unique challenges,such as distinct traffic patterns during vacations and the increased demand for long-term forecastings.Consequently,the effectiveness of existing methods diminishes in such scenarios.Therefore,we propose a novel longterm forecasting model based on scene matching and embedding fusion representation to forecast long-term holiday traffic flow.Our model comprises three components:the similar scene matching module,responsible for extracting Similar Scene Features;the long-short term representation fusion module,which integrates scenario embeddings;and a simple fully connected layer at the head for making the final forecasting.Experimental results on real datasets demonstrate that our model outperforms other methods,particularly in medium and long-term forecasting scenarios.
文摘Urban grid power forecasting is one of the important tasks of power system operators, which helps to analyze the development trend of the city. As the demand for electricity in various industries is affected by many factors, the data of relevant influencing factors are scarce, resulting in great deviations in the accuracy of prediction results. In order to improve the prediction results, this paper proposes a model based on Multi-Target Tree Regression to predict the monthly electricity consumption of different industrial structures. Due to few data characteristics of actual electricity consumption in Shanghai from 2013 to the first half of 2017. Thus, we collect data on GDP growth, weather conditions, and tourism season distribution in various industries in Shanghai, model and train the electricity consumption data of different industries in different months. The multi-target tree regression model was tested with actual values to verify the reliability of the model and predict the monthly electricity consumption of each industry in the second half of 2017. The experimental results show that the model can accurately predict the monthly electricity consumption of various industries.
文摘In semiconductor and electronics factories, large multi-chiller systems are needed to satisfy strict cooling load requirements. In order to save energy, it is worthwhile to design the chilled water system operation. In this paper, an optimal flexible operation scheme is developed based on a two-dimensional time-series model to forecast the cooling load of multi-chiller systems with chiller units of different cooling capacities running in parallel. The optimal integrity scheme can be obtained using the Mixed Integer Nonlinear Programming method, which minimizes the energy consumption of the system within a future time period. In order to better adapt the change of cooling load, the operation strategy of regulating the chilled water flowrates is employed. The chilled water flowrates are set as a design variable. When the chillers are running, their chilled water flowrates can vary within limits, whereas the flowrates are zero when the chillers are unloaded. This forecasting method provides integral optimization within a future time period and offers the operating reference for operators. The power and advantages of the proposed method are presented using an industrial case to help readers delve into this matter.
文摘<div style="text-align:justify;"> <span style="font-family:Verdana;">Sensitivity analysis of neural networks to input variation is an important research area as it goes some way to addressing the criticisms of their black-box behaviour. Such analysis of RBFNs for hydrological modelling has previously been limited to exploring perturbations to both inputs and connecting weights. In this paper, the backward chaining rule that has been used for sensitivity analysis of MLPs, is applied to RBFNs and it is shown how such analysis can provide insight into physical relationships. A trigonometric example is first presented to show the effectiveness and accuracy of this approach for first order derivatives alongside a comparison of the results with an equivalent MLP. The paper presents a real-world application in the modelling of river stage shows the importance of such approaches helping to justify and select such models.</span> </div>
文摘径流过程呈现出的强非线性,使得现有水文模型的预测性能受到制约,深度学习等人工智能方法具有较强的非线性拟合能力,一定程度上可以突破现有瓶颈。为有效提取径流序列的非线性时变特征信息,提高径流模拟精度和多步预测性能,以雅砻江上游雅江流域为研究对象,建立了基于具有时变结构的ForecastNet径流预测模型,并与传统水文模型SWAT(Soil and Water Assessnent Teol)和神经网络模型RNN(Recurrent Neural Network)、LSTM(Long Short-Term Memory)及其组合进行对比分析。结果表明,ForcastNet模型在长预见期径流预测中有较强的适用性,能有效提高径流模拟及多步预测精度,为高精度实时径流预测提供了一种技术支撑。
基金The National Nat-ural Science Foundation of China (NSFC), Grant Nos.90711003, 40375014the program of GYHY200706005, and the APCC Visiting Scientist Program jointly supportedthis work.
文摘The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient （ACC） has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble （MME） forecast.
基金supported by the National Natural Science Foundation of China(7117111370901041)
文摘This paper aims to study a novel expansion discrete grey forecasting model, which could aggregate input information more effectively. In general, existing multi-factor grey forecasting models, such as one order and h variables grey forecasting model （GM （1, h））, always aggregate the main system variable and independent variables in a linear form rather than a nonlinear form, while a nonlinear form could be used in more cases than the linear form. And the nonlinear form could aggregate collinear independent factors, which widely lie in many multi-factor forecasting problems. To overcome this problem, a new approach, named as the Solow residual method, is proposed to aggregate independent factors. And a new expansion model, feedback multi-factor discrete grey forecasting model based on the Solow residual method （abbreviated as FDGM （1, h））, is proposed accordingly. Then the feedback control equation and the parameters＇ solution of the FDGM （1, h） model are given. Finally, a real application is used to test the modelling accuracy of the FDGM （1, h） model. Results show that the FDGM （1, h） model is much better than the nonhomogeneous discrete grey forecasting model （NDGM） and the GM （1, h） model.
文摘[Objective] The aim was to study the refined forecast method of daily highest temperature in Wugang City from July to September. IM[ethod] By dint of ECMWF mode product and T231 in 2009 and 2010 and daily maximum temperature in the station in corresponding period, multi-factors similar forecast method to select forecast sample, multivariate regression multi-mode integration MOS method, after dynamic corrected mode error and regression error, dynamic forecast equation was concluded to formulate the daily maximum temperature forecast in 24 -120 h in Wugang City from July to September. [ Result] Through selection, error correction, the daily maximum temperature equation in Wugang City from July to September was concluded. Through multiple random sampling, F test was made to pass test with significant test of 0.1. [ Conclusionl The method integrated domestic and foreign forecast mode, made full use of useful information of many modes, absorbed each others advantages, con- sidered local regional environment, lessen mode and regression error, and improved forecast accuracy.
文摘The reformation of the economy system has led the f un ctional department and status of the enterprises into a variable state. Under th e condition of the market economy, the kernel of the enterprises’ functional dep artment has diverted to that of marketing decision-making, which face to market and meet with the need of consumption. Assuredly, the kernel of marketing decis ion-making is to prognosticate the future market demand of the production of en terprises accurately, so that it can ensure and realize the maximum of the enter prises’ profit increase. Using empirical research and the multi-regression technique, this paper ana lyzes the enterprises’ production demand forecast of the GMC (Global Management Challenge, held every year globally) and changes most of uncontrollable factors of demand forecast to the controllable ones of the enterprises. The method we us ed to forecast demand by using the multi-regression technique is as follows: 1. Look for the main factors which influence the demand of productions; 2. Establish the regression model; 3. Using the historical data, find the resolution of the correlative index an d do the prominent test; 4. Analyze and compare, regression, adjust parameter and optimize the regress ion model. Our method will make the forecast data closer to the actual prices of the future market requirement quantity in the production marketing decision-making of the enterprises and realize the optimizing combination and the working object w ith the minimum of the cost and the maximum of the profit. And it can ensure the realization of the equity maximum of the enterprises and increase the lifecycle of the production.
文摘For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machine（LS-SVM） is presented. The multi-agent genetic algorithm（MAGA） is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision,calculation speed and stability.
基金This research was performed for the project "Development of technique for Local Prediction", one of the research and development projects on meteorology and seismology funded by the Korea Meteorological Administration (KMA), 2005.
文摘The objective of this study is to improve the statistical modeling for the ternary forecast of heavy snowfall in the Honam area in Korea. The ternary forecast of heavy snowfall consists of one of three values, 0 for less than 50 mm, 1 for an advisory （50-150 ram）, and 2 for a warning （more than 150 mm）. For our study, the observed daily snow amounts and the numerical model outputs for 45 synoptic factors at 17 stations in the Honam area during 5 years （2001 to 2005） are used as observations and potential predictors respectively. For statistical modeling and validation, the data set is divided into training data and validation data by cluster analysis. A multi-grade logistic regression model and neural networks are separately applied to generate the probabilities of three categories based on the model output statistic （MOS） method. Two models are estimated by the training data and tested by the validation data. Based on the estimated probabilities, three thresholds are chosen to generate ternary forecasts. The results are summarized in 3 × 3 contingency tables and the results of the three-grade logistic regression model are compared to those of the neural networks model. According to the model training and model validation results, the estimated three-grade logistic regression model is recommended as a ternary forecast model for heavy snowfall in the Honam area.
