Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events.A novel short-term forecasting method named TIK was proposed,in which ARMA forecasting model was used t...Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events.A novel short-term forecasting method named TIK was proposed,in which ARMA forecasting model was used to consider the load time series trend forecasting,intelligence forecasting DESVR model was applied to estimate the non-linear influence,and knowledge mining methods were applied to correct the errors caused by irregular events.In order to prove the effectiveness of the proposed model,an application of the daily maximum load forecasting was evaluated.The experimental results show that the DESVR model improves the mean absolute percentage error(MAPE) from 2.82% to 2.55%,and the knowledge rules can improve the MAPE from 2.55% to 2.30%.Compared with the single ARMA forecasting method and ARMA combined SVR forecasting method,it can be proved that TIK method gains the best performance in short-term load forecasting.展开更多
In order to improve the efficiency of automatic management and self-healing of the self-organizing network(SON),a cell outage problem is investigated and a cooperative prediction-based automatic cell outage detection ...In order to improve the efficiency of automatic management and self-healing of the self-organizing network(SON),a cell outage problem is investigated and a cooperative prediction-based automatic cell outage detection algorithm is proposed.By the improved collaborative filtering prediction algorithm,the location correlation of users in the wireless network is considered.By incorporating the cooperative grey model prediction algorithm,the time correlation of users motion trajectory is also introduced.Data of users in a normal scenario is simulated and collected for model training and threshold calculating and the outage cell can be effectively detected using the proposed approach.The simulation results demonstrate that the proposed scheme has a higher detection rate for different extents of outage while ensuring the lower communication overhead and false alarm rate than traditional outage detection methods.The detection rate of the proposed approach outperforms the traditional method by around 14%,especially when there are sparse users in the network,and it is able to detect the outage cell with no active users with the help of neighbor cells.展开更多
Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most ...Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users' buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized.Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then,two factor-user matrices can be used to construct a so-called ‘preference dictionary' that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.展开更多
基金Projects(70671039,71071052) supported by the National Natural Science Foundation of ChinaProjects(10QX44,09QX68) supported by the Fundamental Research Funds for the Central Universities in China
文摘Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events.A novel short-term forecasting method named TIK was proposed,in which ARMA forecasting model was used to consider the load time series trend forecasting,intelligence forecasting DESVR model was applied to estimate the non-linear influence,and knowledge mining methods were applied to correct the errors caused by irregular events.In order to prove the effectiveness of the proposed model,an application of the daily maximum load forecasting was evaluated.The experimental results show that the DESVR model improves the mean absolute percentage error(MAPE) from 2.82% to 2.55%,and the knowledge rules can improve the MAPE from 2.55% to 2.30%.Compared with the single ARMA forecasting method and ARMA combined SVR forecasting method,it can be proved that TIK method gains the best performance in short-term load forecasting.
基金The National Natural Science Foundation of China(No.61571123,61521061)the Research Fund of National Mobile Communications Research Laboratory of Southeast University(No.2018A03,2019A03)+1 种基金the National Major Science and Technology Project(No.2017ZX03001002-004)the 333 Program of Jiangsu Province(No.BRA2017366)
文摘In order to improve the efficiency of automatic management and self-healing of the self-organizing network(SON),a cell outage problem is investigated and a cooperative prediction-based automatic cell outage detection algorithm is proposed.By the improved collaborative filtering prediction algorithm,the location correlation of users in the wireless network is considered.By incorporating the cooperative grey model prediction algorithm,the time correlation of users motion trajectory is also introduced.Data of users in a normal scenario is simulated and collected for model training and threshold calculating and the outage cell can be effectively detected using the proposed approach.The simulation results demonstrate that the proposed scheme has a higher detection rate for different extents of outage while ensuring the lower communication overhead and false alarm rate than traditional outage detection methods.The detection rate of the proposed approach outperforms the traditional method by around 14%,especially when there are sparse users in the network,and it is able to detect the outage cell with no active users with the help of neighbor cells.
基金supported by the National Basic Research Program(973)of China(No.2012CB316400)the National Natural Science Foundation of China(No.61571393)
文摘Generally, predicting whether an item will be liked or disliked by active users, and how much an item will be liked, is a main task of collaborative filtering systems or recommender systems. Recently, predicting most likely bought items for a target user, which is a subproblem of the rank problem of collaborative filtering, became an important task in collaborative filtering. Traditionally, the prediction uses the user item co-occurrence data based on users' buying behaviors. However, it is challenging to achieve good prediction performance using traditional methods based on single domain information due to the extreme sparsity of the buying matrix. In this paper, we propose a novel method called the preference transfer model for effective cross-domain collaborative filtering. Based on the preference transfer model, a common basis item-factor matrix and different user-factor matrices are factorized.Each user-factor matrix can be viewed as user preference in terms of browsing behavior or buying behavior. Then,two factor-user matrices can be used to construct a so-called ‘preference dictionary' that can discover in advance the consistent preference of users, from their browsing behaviors to their buying behaviors. Experimental results demonstrate that the proposed preference transfer model outperforms the other methods on the Alibaba Tmall data set provided by the Alibaba Group.
