Location prediction based on a sector snapshot for location-based services

In location-based services (LBSs), the service is provided based on the users' locations through location determination and mobility realization. Most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shaped cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the new Markov-based mobility prediction (NMMP) and prediction location model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression, and insufficient accuracy. In this paper, a novel cell splitting algorithm is proposed. Also, a new prediction technique is introduced. The cell splitting is universal so it can be applied to all types of cells. Meanwhile, this algorithm is implemented to the Micro cell in parallel with the new prediction technique. The prediction technique, compared with two classic prediction techniques and the experimental results, show the effectiveness and robustness of the new splitting algorithm and prediction technique

New in-sample prediction errors in time series with applications

^aThis article introduces two new types of prediction errors in time series: the filtered prediction errors and the deletion prediction errors. These two prediction errors are obtained in the same sample used for estimation, but in such a way that they share some common properties with out of sample prediction errors. It is proved that the filtered prediction errors are uncorrelated, up to terms of magnitude order O(T^-2), with the in sample innovations, a property that share with the out-of-sample prediction errors. On the other hand, deletion prediction errors assume that the values to be predicted are unobserved, a property that they also share with out-of-sample prediction errors. It is shown that these prediction errors can be computed with parameters estimated by assuming innovative or additive outliers, respectively, at the points to be predicted. Then the prediction errors are obtained by running the procedure for all the points in the sample of data. Two applications of these new prediction errors are presented. The first is the estimation and comparison of the prediction mean squared errors of competing predictors. The second is the determination of the order of an ARMA model. In the two applications the proposed filtered prediction errors have some advantages over alternative existing methods.

Nonlinear predictive control applied to steam/water loop in large scale ships

In steam/water loop for large scale ships, there are mainly five sub-loops posing different dynamics in the complete process. When optimization is involved, it is necessary to select different prediction horizons for each loop. In this work, the effect of prediction horizon for Multiple-Input Multiple-Output (MIMO) system is studied. Firstly, Nonlinear Extended Prediction Self-Adaptive Controller (NEPSAC) is designed for the steam/water loop system. Secondly, different prediction horizons are simulated within the NEPSAC algorithm. Based on simulation results, we conclude that specific tuning of prediction horizons based on loop’s dynamic outperforms the case when a trade-off is made and a single valued prediction horizon is used for all the loops

Finding missing edges in networks based on their community structure

Many edge prediction methods have been proposed, based on various local or global properties of the structure of an incomplete network. Community structure is another significant feature of networks: Vertices in a community are more densely connected than average. It is often true that vertices in the same community have "similar" properties, which suggests that missing edges are more likely to be found within communities than elsewhere. We use this insight to propose a strategy for edge prediction that combines existing edge prediction methods with community detection. We show that this method gives better prediction accuracy than existing edge prediction methods alone.Comment: 7 pages, 6 figure