Deep learning methods have become increasingly popular for time series nowcasting; however, their effectiveness is limited when making predictions on out-of-distribution data in dynamic streaming environments. This limitation is mainly caused by concept drifts, which occur when the underlying data distribution changes over time – and proves particularly challenging in multi-stream settings, where numerous streams of data exist without necessarily exhibiting the same dynamic behavior. When confronted with concept drift, since many parts of the neural network may still contain useful knowledge about the relevant domain, re-training only on new data poses the risk of catastrophic forgetting. On the other hand, re-training on old and new data can incur significant computational overhead and latency, which is not desirable in a streaming context. Conversely, stream learning allows models to adapt quickly to the new distribution; however, it often fails to achieve efficiency comparable to batch learning. This work aims to combine the strengths of neural networks and streaming regressors for data stream nowcasting in two key ways. First, a streaming regressor utilizes the higher-order representations learned by the neural network, which is set up as a feature extractor and a regressor. Second, the streaming regressor predicts the residual of the neural network. Experiments conducted on challenging real-world dynamic multi-streaming data indicate that this hybrid approach provides better results than the traditional static neural network.