Context. The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data. Such spatial trajectory data accumulated by telecommunication operators is huge, analyzing the data with a right tool or method can uncover patterns and connections which can be used for improving telecom services. Forecasting trajectory data or predicting next location of users is one of such analysis. It can be used for producing synthetic data and also to determine the network capacity needed for a cell tower in future.

Objectives. The objectives of this thesis is, Firstly, to have a new application for CWT (Collapsed Weighted Tensor) method. Secondly, to modify the CWT method to predict the location of a user. Thirdly, to provide a suitable method for the given Telenor dataset to predict the user’s location over a period of time.

Methods. The thesis work has been carried out by implementing the modified CWT method. The predicted location obtained by modified CWT cannot be determined to which time stamp it belongs as the given Telenor dataset contains missing time stamps. So, the modified CWT method is implemented in two different methods.

1. Replacing missing values with first value in dataset.
2. Replacing missing values with second value in dataset.

These two methods are implemented and determined which method can predict the location of users with minimal error.

Results. The results are carried by assuming that the given Telenor dataset for one week will be same as that for the next week. Users are selected in a random sample and above mentioned methods are performed. Furthermore, RMSD values and computational time are calculated for each method and selected users.

Conclusion. Based on the analysis of the results, Firstly, it can be concluded that CWT method have been modified and used for predicting the user’s location for next time stamp. Secondly, the method can be extended to predict over a period of time. Finally, modified CWT method predicts location of the user with minimal error when missing values are replaced by first value in the dataset.