Choreographic modeling, that is identification of key choreographic primitives, is a significant element for Intangible CulturalHeritage (ICH) performing art modeling. Recently, deep learning architectures, such as LSTM and CNN, have been utilized forchoreographic identification and modeling. However, such approaches present sensitivity to capturing errors and fail to model thedynamic characteristics of a dance, since they assume a stationarity between the input-output data. To address these limitations,in this paper, we introduce an AutoRegressive Moving Average (ARMA) filter into a conventional CNN model; this means thatthe classification output feeds back to the input layer, improving overall classification accuracy. In addition, an adaptive imple-mentation algorithm is introduced, exploiting a first-order Taylor series expansion, to update network response in order to fit dancedynamic characteristics. This way, the network parameters (e.g., weights) are dynamically modified improving overall classificationaccuracy. Experimental results on real-life dance sequences indicate the out-performance of the proposed approach with respect toconventional deep learning mechanisms.