The increasing amount of information makes sequential data mining an important domain of research. A vast number of data mining models and approaches have been developed in order to extract interesting and useful patterns of data. Most models are used for strategic purposes resulting in using of the time parameter. However, the extensive field of data mining applications requires new models to be introduced. The current thesis proposed models for temporal sequential data mining having as a goal the forecasting process. We focus our study on sequential temporal database analysis and on time-series data. In sequential database analysis we propose several interestingness measures for rules selection and patterns extraction. Their goal is to advantage those rules/patterns whose time-distance between the itemsets is small. The extracted information is used to predict user¿s future requests in a web log database, obtaining a higher performance in comparison to other compared models. In time-series analysis we propose a forecasting model based on Neural Networks, Genetic Algorithms, and Wavelet Transform. We apply it on a WiMAX network traffic and EUR/USD currency exchange data in order to compare its prediction performance with those obtained using other existing models. Different ways of changing parameters adapted to a given situation and the corresponding simulations are presented. It was shown that the proposed model outperforms the existing ones from the prediction point of view on the used time-series. As a whole, this thesis proposes forecasting models for different types of temporal sequential data with different characteristics and behaviour.