This study proposes a collection of automated forecasting approaches for quality of service (QoS) attributes of Web services. Each one of these approaches is able to fit and forecast a specific type of QoS stochastic characteristics, however, taken together they will be able to fit different dynamic behaviors of QoS attributes and forecast their future values. In particular, the thesis proposes an automated forecasting approach for nonlinearly dependent QoS attributes, two automated forecasting approaches for linearly dependent QoS attributes with volatility clustering, and two automated forecasting approaches for nonlinearly dependent QoS attributes with volatility clustering. The accuracy and performance of the proposed forecasting approaches are evaluated and compared to those of the baseline ARIMA time series models using real-world QoS datasets of Web services characterized by nonlinearity and volatility clustering. The evaluation results show that each one of the proposed forecasting approaches outperforms the baseline ARIMA models.