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From weather to networks, forecasting techniques constitute an interesting challenge: rather than giving a faithful description of the current reality, as a looking glass would do, researchers seek crystal-ball models to speculate on the future. This work explores the use of Support Vector Regression (SVR) for the purpose of link load forecast. SVR works well in many learning situations, because they generalize to unseen data, and are amenable to continuous and adaptive online learning - an extremely desirable property in network environments. Motivated by the encouraging results recently gathered by means of SVR on other networking applications, our aim is to enlighten whether SVR is also successful for the prediction of network links load at short time scales. We consider the problem of link load forecast based only on its past measurements, which is referred to as ''embedded process'' regression in the SVR lingo, and adopt a hands-on approach to evaluate SVR performance. In more detail, we perform a sensitivity analysis of the parameters involved, assess the computational complexity for training and validation, dig into the correlation structure of the prediction errors and evaluate techniques to extend the forecasting horizon. Our finding is that accuracy results are close enough to be tempting, but not enough to be convincing. Yet, as SVR exhibit a number of advantages, such as good robustness and flexibility properties, furthermore at a price of a limited complexity, we then speculate on what directions can be undertaken to ameliorate its performance in this context.