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Availability of several web services having a similar functionality has led to using quality of service (QoS) attributes to support services selection and management. To improve these operations and be performed proactively, time series ARIMA models have been used to forecast the future QoS values. However, the problem is that in this extremely dynamic context the observed QoS measures are characterized by a high volatility and time-varying variation to the extent that existing ARIMA models cannot guarantee accurate QoS forecasting where these models are based on a homogeneity (constant variation over time) assumption, which can introduce critical problems such as proactively selecting a wrong service and triggering unrequired adaptations and thus leading to follow-up failures and increased costs. To address this limitation, we propose a forecasting approach that integrates ARIMA and GARCH models to be able to capture the QoS attributes' volatility and provide accurate forecasts. Using QoS datasets of real-world web services we evaluate the accuracy and performance aspects of the proposed approach. Results show that the proposed approach outperforms the popular existing ARIMA models and improves the forecasting accuracy of QoS measures and violations by on average 28.7% and 15.3% respectively.