Human pose forecasting is a sequential modeling task that aims to predict future poses from historical motions. Most existing approaches focus on the spatial-temporal neural network model design for learning movement patterns to reduce prediction errors. However, they usually do not strictly follow the temporal constraints in the inference stage. Even though a small Mean Per Joint Position Error (MPJPE) is achieved, some of the predicted poses are not temporal feasible solutions, which disobeys the continuity of the body movement. In this paper, we consider the temporal constrained feasible solutions for human pose forecasting, where the predicted poses of input historical poses are guaranteed to obey the temporal constraints strictly in the inference stage. Rather than direct supervision of the prediction in the original pose space, a temporal constrained subspace is explicitly learned and then followed by an inverse transformation to obtain the final predictions. We evaluate the proposed method on large-scale benchmarks, including Human3.6M, AMASS, and 3DPW. State-of-the-art performance has been achieved with the temporal constrained feasible solutions.