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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Journal of Industrial and Systems Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Adaptive neuro-fuzzy inference system,auto-regressive integrated moving average,global gold price
- چکیده:
- چکیده انگلیسی: Proper models for prediction of time series data can be an advantage in making important decisions. In this study, we tried with the comparison between one of the most useful classic models of economic evaluation, auto-regressive integrated moving average model and one of the most useful artificial intelligence models, adaptive neuro-fuzzy inference system (ANFIS), investigate modeling procedure of these methods. Furthermore, we analyzed the performance of these methods in prediction the global gold price. For this purpose, 200 gold price data from February 2015 to October 2015 were gathered. We used both methods for determination of model parameters then we predicted the test data. With respect to reliable standards of evaluation prediction as root mean square of errors, it was seen that in time series data, prediction of adaptive neuro-fuzzy inference system model is more accurate than the auto-regressive integrated moving average model. So we can conclude that at least in some cases where time series have a non-linear trend, it is better to use adaptive neuro-fuzzy inference system model for prediction. In this manner, we can reach our goals in future with higher accuracy in our decisions, in future.
- انتشار مقاله: 18-12-1394
- نویسندگان: Kazem Noghondarian,Emran Mohammadi,Ali Shahrabi Farahani
- مشاهده
- جایگاه : پژوهشی
- مجله: Advances in Mathematical Finance and Application
- نوع مقاله: Journal Article
- کلمات کلیدی: Insurance Companies,Stocks Ranking,Fuzzy DEA
- چکیده:
- چکیده انگلیسی: The main goal of this paper is to propose a new approach for efficiency measurement and ranking of stocks. Data envelopment analysis (DEA) is one of the popular and applicable techniques that can be used to reach this goal. However, there are always concerns about negative data and uncertainty in financial markets. Since the classical DEA models cannot deal with negative and imprecise values, in this paper, possibilistic range directional measure (PRDM) model is proposed to measure the efficiencies of stocks in the presence of negative data and uncertainty with input/output parameters. Using the data from insurance industry, this model is also implemented for a real case study of Tehran stock exchange (TSE) in order to analyse the performance of the proposed method.
- انتشار مقاله: 27-09-1397
- نویسندگان: Pejman Peykani,Emran Mohammadi,Mohsen Rostamy-Malkhalifeh,Farhad Hosseinzadeh Lotfi
- مشاهده