Reservoir computing has emerged as a practical paradigm of implementing neural network algorithms on hardware for high-efficient computing. With the concept of reservoir computing, various electronic\' dynamics can be harvested as computational resources, which has received considerable attention in recent years. Meanwhile, dynamic memristor is an emerging memristive device that exhibiting interesting biomimetic behaviours such as short-term memory. Moreover, its conductance state can be varied by historical stimulation. In this work, a reservoir computing model using $rm TiOx-based dynamic memristor as processing core is proposed. The dynamic memristor is measured and characterised, followed by using the discrete model to approximate the behaviours of the dynamic memristor. Finally, a parallel dynamic memristor reservoir computer is simulated based on the dynamic memristor model. This model is evaluated by a waveform classification. The results (normalized root mean square error is 0.15 when using 10 dynamic memristors) indicate the feasibility of using the physical behaviours of dynamic memristor for constructing reservoir computers.