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ن (١٣٩٢) ٣٧ ٣3-1٩ تورد 8/E / &E,G, &(.,6 E & [) - *de (.,6 8B9.4 $(% MAPE &7D A./. U K,) 2/6799 2/7104 2/8885 2/2026 = 0 / 8 = 0 / 05 =4 = 0 / 8 = 0 / 05 =6 = 0 / 3 =35! =10 =0 " =1 = 1 / 2 h = 0 / 5 &U F.E &"$/) [) -*de U * I (4)[) -!.^ (6)[) -!.^ 8.' (/5#,1 [) 8" [) -*de (4) (3) UG F 8,fK/.* 84. &8B9 (3) UG E, &U AE *,. -# GF ˆ' K,F F ET *W EF,i 8E :.'1 W E, F 0b',i A 8E.(,/ m. [) -*de & U.,G &8B9 U & [) - *de U AE &5 Og.D : H.*,G, &8B9 U [) - *de & U K 5 'g.d : H 1.* T/ 8E F UG K,F A r4 F V3` a. K 0EN / &E *,. -# K,F a..5 $(% &1 84. p-value t test 8 U. 5 OR A U $ %&' > $ )** 0/0000616 6/0526 [) -*de 0/0858 1/9099 (4)[) -!.^ $ %&+ > $ )** 0/0238 2/2571 0/5145 2/2807 (6)[) -!.^ $ %,** > $ )** 0/000185 8/4978 0/0091 1/6938 8.' (/5#,1 [) 8" 1/6102 [) -*de 8/E / &E *,. -# K,F a..6 $(% &1 84. p-value t test 8 U. 5 OR A U $ %&' > $ )** 0/1521 1/1232 [) -*de 1/0403 3/7171 (4)[) -!.^ $ %&+ > $ )** 0/0782 1/6194 1/0754 4/1223 (6)[) -!.^ $ %,** > $ )** 0/5006 0/0017 1/1901 2/8885 8.' (/5#,1 [) 8" 2/8908 [) -*de 47

AB $ و?@ <ر=ن < EN9:; = EF G! ; H $ IJK L $ M U ز=ر VD RS م OPQ =ن C F && ET &#]E *W [) -*de & U 8 K I ˆ' K,F a.. 8 (e,e FE K,t() YE, RA >' 4.E 6 6, &FE I' *' P* H$G,D 8E &F 8G,# 8 &86B CD 9 89$ E B 8 * A 6 6, &FE K11 $3 O F " K,( SN# &?18 H &*3' P FE A,.E L, MN. 8 6 8E K,#. A/./ HB,,.E QR3 SN# 0,3 6 6, &FE 8E KNE L,#,i 8E 6 L,. &IE `f K &*6 A#( F " 5E 8 (/[# A 0u# F &86 &1(/[# */ *$) 8E 6 ` F,`.E HG,? U. 0u# A E *$EW., 8.'1 i P ƒ * A"/ &1(/[# F &"$/) U. `f &E. &(.B fd A/ 8E.,D &1(/[# MN.,E. ET 8E F,D #g"#.fne e,e FE E T 8E /, 6 &85,# E #,/ >g# K1B, b` 869 "$/) K,F m. ),3, [) &8" >F, &E *de &".E' (.,6 F. A/ &U F.E &"$/) & /, U 8 K K# e,e FE F / & E K. \$7 )5 8 8" 8.' (/5#,1 [) 8" [) -!.^ K,t( &v9 9 [1] Hadavandi, E., H. Shavandi, A., (2010). Ghanbari, Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowledge Based System, 23, 800 808. [2] Hadavandi, E., et al., (2011). Tourist arrival forecasting by evolutionary fuzzy systems. Tourism Management, 32(5), 1196-1203. [3] Efendigil, T., Önüt, S., Kahraman, C., (2009). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems with Applications, 36(3), 6697-6707. [4] Azadeh, A., Saberi, M., Asadzadeh, S. M., (2011). An adaptive network based fuzzy inference system auto regression analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada, United Kingdom, and South Korea. Applied Mathematical Modelling. 35(2), 581-593. [5] Konar, (2005). Computational Intelligence: Principles, techniques. Berlin. Springer. [6] Khashei, M., Bijari, M., (2010). An artificial neural network ) p, d,q) model for timeseries forecasting. Expert Systems with Applications, 37, 479 489. [7] Khashei, M., Hejazi, S. R., Bijari, M., (2008). A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. 159, 769-789. [8] Virtanen, I., Yli-Olli, P., (1990). Working's effect revisited Fitting univariate time series to stock price data. Omega. 18(3), 229-230. [9] Grillenzoni, C., (1998). Forecasting unstable and nonstationary time series. International Journal of Forecasting, 14(4). 469-482. 48

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