جستجوی مقالات مرتبط با کلیدواژه « forecast » در نشریات گروه « صنایع »

Due to the increasing importance of marketing, entrepreneurship and the role of organizational structure in their application, the purpose of this research is to predict entrepreneurial marketing using an organizational structure in the insurance industry. For this purpose, for marketing, seven indicators and for organizational structure, three indicators are defined, then prediction of entrepreneurial marketing indicators has been done by organizational structure indicators using lazy learning algorithm. In the proposed method, after predicting each data by K vector from its closest neighbor, the algorithm database is enriched for better prediction of future data. The proposed algorithm is simulated and compared in five different modes by MATLAB software, also, three insurance (Iran, Karafarin and Parsiyan) companies are selected in Mazandaran province. In total, the statistical population in this study is 588 cases. The results of simulation indicate the proper accuracy of entrepreneurial marketing forecasting based on validation parameters MSE and NRMSD. In this research, Lazy Learning method can predict future without modeling the problem with previous information processing.

In the banking industry, there is intense competition between banks to attract resources and facilities. With the development of new services, bank managers try to improve their services and attract more customer deposits by differentiating between competitors' services. This research uses a two-stage TOPSIS method with the combination of neural network model and Monte Carlo simulation trading method to analyze and compare bank productivity forecasts with the 4 efficiency criteria of the banking industry. TOPSIS was first used in two steps to rate the efficiency of banks and then a model was created for banking performance with clear forecasting ability. Secondly, an MCMC sampling method and ANN training was presented. Integrated neural networks and MCMCs were used which are consistent with TOPSIS results. The simulation effect of the selected variables was predicted and their effect on performance was observed. The proposed method was used successfully for predicting performance and ranking banks based on the relative importance of performance criteria expressed by considering the performance levels in the TOPSIS method. Then, the artificial neural network was modeled using the results obtained from the TOPSIS method, an effective model for appropriate prediction of bank performance. Based on the results of the proposed model and the level of importance of performance measures, cost and revenue structure were considered to be the main causes of inefficiency