In the operation and maintenance of high-end manufacturing enterprises， the spare parts demand occurs randomly， accompanied by a large number of zero demand periods. At the same time， the corresponding sparse parts demand data is of small scale and has intermittent and distribution with lump formation characteristics. Consequently， most of current time series forecasting methods are hard to effectively predict the demand trends. To solve this problem， a predictability evaluation and joint forecasting method for intermittent time series was proposed. Firstly， a new intermittent-similarity metric was proposed. In this metric， the frequency and positions of the "0" element occurring in the two sequences were counted， while the metrics such as maximal information coefficient and average demand interval were combined to evaluate the tendency information and fluctuation pattern of the sequences effectively and realize the quantification of the predictability of the intermittent time series. Then， based on this metric， an intermittent-similarity hierarchical clustering method was constructed to adaptively select the sequences with high similarity and strong predictability as well as eliminate extremely sparse and unpredictable sequences. Moreover， the structured information between the sequences was explored and utilized， a Multi-output Support Vector Regression （M-SVR） model was constructed， thereby achieving the joint prediction of intermittent time series with small-scale data. Finally， the experiments were conducted on two public datasets （UCI （University of California Irvine） gift retail dataset and Huawei computer accessory dataset） and a real-world spare parts after-sales dataset of a large manufacturing enterprise， respectively. The results show that compared with several representative time series forecasting methods， the proposed method can effectively exploit the predictability of various kinds of intermittent sequences and improve the prediction accuracy of intermittent time series with small-scale data. Therefore， the proposed method provides a new solution for the spare parts demand forecasting of manufacturing enterprises.

At present， Generative Adversarial Network （GAN） has been used for image animation style transformation. However， most of the existing GAN-based animation generation models mainly focus on the extraction and generation of realistic style with the targets of Japanese animations and American animations. Very little attention of the model is paid to the transfer of impressionistic style in Chinese-style animations， which limits the application of GAN in the domestic animation production market. To solve the problem， a new Chinese-style animation GAN model， namely Chinese Cartoon GAN （CCGAN）， was proposed for the automatic generation of animation videos with Chinese impressionistic style by integrating Chinese impressionistic style into GAN model. Firstly， by adding the inverted residual blocks into the generator， a lightweight deep neural network model was constructed to reduce the computational cost of video generation. Secondly， in order to extract and transfer the characteristics of Chinese impressionistic style， such as sharp image edges， abstract content structure and stroke lines with ink texture， the gray-scale style loss and color reconstruction loss were constructed in the generator to constrain the high-level semantic consistency in style between the real images and the Chinese-style sample images. Moreover， in the discriminator， the gray-scale adversarial loss and edge-promoting adversarial loss were constructed to constrain the reconstructed image for maintaining the same edge characteristics of the sample images. Finally， the Adam algorithm was used to minimize the above loss functions to realize style transfer， and the reconstructed images were combined into video. Experimental results show that， compared with the current representative style transfer models such as CycleGAN and CartoonGAN， the proposed CCGAN can effectively learn the Chinese impressionistic style from Chinese-style animations such as Chinese Choir and significantly reduce the computational cost， indicating that the proposed CCGAN is suitable for the rapid generation of animation videos with large quantities.

The traditional sparse adaptive filtering has the problems of poor steady-state performance and even unable to converge in impulse noise interface environment. In order to solve the problems and improve the accuracy of sparse parameter identification without increasing too much computational cost， a sparse adaptive filtering algorithm based on Generalized Maximum Versoria Criterion （GMVC） was proposed， namely the GMVC with CIM constraints （CIMGMVC）. Firstly， the generalized Versoria function was employed as the learning criterion， which contained the reciprocal form of the error p-order moment. And thus the purpose of suppressing impulse noise was able to be achieved because the GMVC would approach to 0 when the error caused by the impulse interference was very large. Then， a novel cost function was constructed by combining the Correntropy Induced Metric （CIM） used as the sparse penalty constraint and the GMVC， where the CIM was based on the Gaussian probability density function， and it was able to be infinitely close to l_{\mathrm{0}} -norm when the appropriate kernel width was selected. Finally， the CIMGMVC algorithm was derived by using the gradient method， and the mean square convergence of the proposed algorithm was analyzed. The simulation was performed on Matlab platform， and the \alpha -stable distribution model was used to generate impulse noise. Experimental results show that， the proposed CIMGMVC algorithm can effectively suppress the interference of non-Gaussian impulse noise， it has the better robustness than the traditional sparse adaptive filtering， and has the steady-state error lower than the GMVC algorithm.