Title: Improving the generalization ability of the chaotic time-series classification models by residual component extraction. Abstract: This paper presents a new method in chaotic time-series classification that improves the generalization ability of the machine learning models. Such models are trained on a certain set of parameters of the nonlinear dynamical systems. Because of the sensitive dependence on initial conditions of such systems, when the parameters change the accuracy of machine learning models drops drastically. In our paper, we improve the generalization ability of the Multivariate LSTM-FCN model, by extracting and training the model on the residual component of the time-series. The steps applied are generic and can be reapplied to any other machine learning method. As an illustrative example, we provide a problem of the computation of the 2D bifurcation diagrams for the oscillatory time-series (periodic or chaotic) obtained as solutions for nonlinear systems of ordinary differential equations. The Multivariate LSTM-FCN network was trained on the time-series set obtained for the plane of parameters $(R_1, R_2) \times (C_1, C_2) \times L = (6, 15.5) \times (2.8, 3.4) \times 1$ and it achieved the accuracy of $96.96\%$. Once the parameters where changed to $(R_1, R_2) \times (C_1, C_2) \times L = (20, 29.5) \times (2.2, 2.8) \times 2$ the accuracy dropped to $34.38\%$. Whereas the accuracy of the model trained on residual components of the same dataset decreased from $96.50\%$ to $65.52\%$ for the new parameters time-series dataset. Furthermore, we show that by taking the mean and variance of the residual component we are able to extract many very interesting features of the 2D bifurcation diagrams using the KMeans, GMM, and OPTICS clustering algorithms.