Identifications and analysis of time series are time consuming, based on trial and error and highly dependent on expert judgments. This is mainly due to the presence of various models for forecasting time series, as well as introducing new techniques for analysis and predictions. In this paper, expert system structure is used to replace traditional methods of model identifications for time series. Firstly, several search engines are defined and analytical methods are specified. Next, the knowledge base is developed such that a proper model can be assigned to each data set. The goodness of fit is then evaluated by mathematical indices. Repeating the process and modifying the responses to account for uncertain situations, will provide a set of models to make the final decision. Lastly, the performance of the proposed expert system is verified by a series of sample data as a case study and the efficiency of the system is approved.
Lotfi, M., & Razavi, H. (2014). An Expert System for Identification of Forecasting Model for Time Series. Advances in Industrial Engineering, 48(Special Issue), 71-82. doi: 10.22059/jieng.2014.51786
MLA
M. Lotfi; H. Razavi. "An Expert System for Identification of Forecasting Model for Time Series". Advances in Industrial Engineering, 48, Special Issue, 2014, 71-82. doi: 10.22059/jieng.2014.51786
HARVARD
Lotfi, M., Razavi, H. (2014). 'An Expert System for Identification of Forecasting Model for Time Series', Advances in Industrial Engineering, 48(Special Issue), pp. 71-82. doi: 10.22059/jieng.2014.51786
VANCOUVER
Lotfi, M., Razavi, H. An Expert System for Identification of Forecasting Model for Time Series. Advances in Industrial Engineering, 2014; 48(Special Issue): 71-82. doi: 10.22059/jieng.2014.51786
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