Optical reservoir computing (ORC) offers advantages, such as high computational speed, low power consumption, and high training speed, so it has become a competitive candidate for time series analysis in recent years. The current ORC employs single-dimensional encoding for computation, which limits input resolution and introduces extraneous information due to interactions between optical dimensions during propagation, thus constraining performance. Here, we propose complex-value encoding-based optoelectronic reservoir computing (CE-ORC), in which the amplitude and phase of the input optical field are both modulated to improve the input resolution and prevent the influence of extraneous information on computation. In addition, scale factors in the amplitude encoding can fine-tune the optical reservoir dynamics for better performance. We built a CE-ORC processing unit with an iteration rate of up to ∼1.2 kHz using high-speed communication interfaces and field programmable gate arrays (FPGAs) and demonstrated the excellent performance of CE-ORC in two time series prediction tasks. In comparison with the conventional ORC for the Mackey–Glass task, CE-ORC showed a decrease in normalized mean square error by ∼75 % . Furthermore, we applied this method in a weather time series analysis and effectively predicted the temperature and humidity within a range of 24 h.