The sustainability online prediction is of great significance for higher horizon time-series prediction in the future, and it embodies higher application value in equipment fault prediction and health management. However, compared with one-step time-series prediction, continuous online prediction faces many uncertainties, including error accumulation and lack of information. To realize continuous online prediction of time-series data in complex systems, this paper proposes a continuous online prediction strategy based on multihorizons transfer (OnMultiHorTS), which is used for continuous online prediction tasks of time-series data. The algorithm aims to use source domain data to provide more effective information for target prediction tasks. However, the time-varying characteristics of time-series data often lead to large differences in data distribution over a long time span, which is difficult to guarantee the assumption that the data are the same distribution. How to construct more effective source domain information based on historical data and existing data, and apply it to the target domain prediction tasks, is one of the focuses of our OnMultiHorTS algorithm. In addition, different from the typical iterative and multistep advance prediction methods, the proposed algorithm regards different prediction tasks as different horizons, which are independent of each other and can explore the correlation between them. Therefore, based on the correlation between horizons, the algorithm develops a set of multihorizons continuous online prediction schemes, which can continuously predict the next step time-series data based on the current observation data. Finally, we test the performance of the algorithm on four common time-series data sets and four aeroengine sensor data sets. At the same time, we test the impact of training step size and prediction step size on the performance of the algorithm. Experiments show that the proposed OnMultiHorTS algorithm has higher prediction accuracy in the time-series data prediction task of complex systems, and reflects higher engineering application value.