an expert system for identification of forecasting model for time series

Authors

مهدی لطفی

مهندسی صنایع- دانشگاه فردوسی مشهد حمیده رضوی

گروه مهندسی صنایع- دانشگاه فردوسی مشهد

abstract

identifications andanalysis of time series are time consuming, based on trial and error and highlydependent on expert judgments. this is mainly due to the presence of variousmodels for forecasting time series, as well as introducing new techniques foranalysis and predictions. in this paper, expert system structure is used toreplace 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 toeach data set. the goodness of fit is then evaluated by mathematical indices.repeating the process and modifying the responses to account for uncertainsituations, 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 sampledata as a case study and the efficiency of the system is approved.

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Journal title:

مهندسی صنایع

جلد ۴۸، شماره Special Issue، صفحات ۷۱-۸۲

Keywords

identifications andanalysis of time series are time consuming based on trial and error and highlydependent on expert judgments. this is mainly due to the presence of variousmodels for forecasting time series as well as introducing new techniques foranalysis and predictions. in this paper expert system structure is used toreplace 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 toeach data set. the goodness of fit is then evaluated by mathematical indices.repeating the process and modifying the responses to account for uncertainsituations 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 sampledata as a case study and the efficiency of the system is approved.

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