Title: ACCTS: an Adaptive Model Training Policy for Continuous Classification of Time Series. Abstract: More and more real-world applications require to classify time series at every time. For example, critical patients should be detected for vital signs and diagnosed at all times to facilitate timely life-saving. For this demand, we propose a new concept, Continuous Classification of Time Series (CCTS), to achieve the high-accuracy classification at every time. Time series always evolves dynamically, changing features introducing the multi-distribution form. Thus, different from the existing one-shot classification, the key of CCTS is to model multiple distributions simultaneously. However, most models are hard to achieve it due to their independent identically distributed premise. If a model learns a new distribution, it will likely forget old ones. And if a model repeatedly learns similar data, it will likely be overfitted. Thus, two main problems are the catastrophic forgetting and the over fitting. In this work, we define CCTS as a continual learning task with the unclear distribution division. But different divisions differently affect two problems and a fixed division rule may become invalid as time series evolves. In order to overcome two main problems and finally achieve CCTS, we propose a novel Adaptive model training policy - ACCTS. Its adaptability represents in two aspects: (1) Adaptive multi-distribution extraction policy. Instead of the fixed rules and the prior knowledge, ACCTS extracts data distributions adaptive to the time series evolution and the model change; (2) Adaptive importance-based replay policy. Instead of reviewing all old distributions, ACCTS only replays the important samples adaptive to the contribution of data to the model. Experiments on four real-world datasets show that our method can classify more accurately than all baselines at every time.