Accurate models for estimation of remaining useful life (RUL) is a key tool to improve productivity and reliability in modern industries. Recent advancements in machine learning and easier availability of data are driving development of data driven prognostic frameworks for estimation of RUL. Direct adoption of learning algorithms is challenging due to inherent characteristics of the domain like non-causal definition of required target, heterogeneity among the units, and temporal dependencies. This paper proposes a prognostic framework that attempts to overcome these using a modified support vector regression (SVR) as its base. SVR is modified with temporal features and an adaptive penalty term that gives higher importance to targets closer to the end-of-life of units. An ensemble of such models is learned on groups of units having similar trajectories. The framework is evaluated on benchmark data set to estimate the RUL of aircraft engines.