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7 ن (١٣٩٢) ٣٧ ٣3-1٩ تورد $' 3-3 E & F 0m., F &BE K0s# 8 8[N F r ` 8 4 F F K,D F &.E "$/) U & &E ),[ [) 8" 8 8." A 8E 8G,# E.( V9.! # m3 8 8E (,/ m. T &FU U. &, F " & E & A.'1 ' 8 * (/I -(/ V# &FU U.,! A.E 0/0 & (8/) 9 A./.E 1/0 0/0 AE &9 8E K V" s# K ` F &' At r4 &E./ VB# I 8B9 V/) A.1 W 1/0 & (8E) 9 U & = ( 9 &8/) / ( &8E &8/) :(.BG E V< 87E - 3AB4-3 7 &g 40 &8 FX F ),[ >, F ( NE K,) 8E ),[ [) &8" & (. B &85,# ( &IE 8E [) &8" F. ƒ 90 &8 K /t "m# 8 E W 8 * & _ BE (. B K B I s.* V# c$.n &F,` /, b & U F m. E [W 8 * & [) &8"!"#.* VB V` &. &F8 KB Is & &8G F DE #,n K,#. 8.' V" # P, K 8 ( 8E V[. >F &` &5# F [) &8" F.* UZ & F KB [) &8" &1.E &1 [) &8" ( *$EW &8 ]) 8" & & : F, 8 E K, # [) & 8" &/5 ( &,.' 8 E 8 ] U [#, t (K &8]) &8] 5# (GD &8]) 8" &GD (&.8] &K 5# 8] V# 2E# " 8 E ), [ [) & 8" F m. E r FE ET F,` 8 [D 0]9 8E 8G,# E m. (., 6 A F T ET &86B &E # ( K E ( RE r TNE ) t.,[# 8E T (.,6 F &83gD 3 V".(/ & E 7D 5 K,F F 5# A 8 m. K 8] K t E [) 8" F T A V # 2E# GD 8] (,/ m. )5 8 V# 2E# F K 8] &E. * 8E F4 9 7DX 2 E,# A /N# &. B1 0, 3 8 E )5 8 [) &8".* 8.'1 8E 7D k,# 8., & E. E.[21] * 8.'1 W m., (.B U. &Eeg 2 G A /N#,# K 8]! E )5 8 &8" 8 ) K,# (1991) E Y.(/ m. [) 8" (BW A F <, 8E 8G,# E?6.[22] E 41

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9 ن (١٣٩٢) ٣٧ ٣3-1٩ تورد DE - ( ' (5-3 8G,# D U t a. K NEu KI [) 8" KF FE */ 8E 8G,# E 0]9 H$G 8" KF &F8E &".E' /, &(.,6 F m. 8E K.* t 8E F,` A 9',,i 8E,G, /, &(.,6 \m$# K,#, &".E' &(.,6 &1 F.E r M,$7 &8[N 2/4# $,f 8E &(.,6 &,# F &1E E SW, O?` 8 K */ 8E F O (*de) >md.' 8 E /.,6 [19] { 2010 U >F, &E K F,G, E r, &(.,6 8E *B K #E &1 8E 8G,# E 8 *' v' A M#FE.W.' I#,$ 200 # I#,$ 20.(,/ m. [) 8" DE F>BG v' E G, >md 8F.[23],E,D [9 # >md F 8$3' A./ F 8$3' &8R 8E (/ 8E 8$3' 8F # V/) A T 6 ~, v' P,!I,D [9 8E >md i (*de) >md! K,) 8E 7 1K PE * m n ƒ c$.n ~, U,f E #m. #,3 U >md. m Dki f k = c1 i = 1 m!i [9 8E >md 8 F, m.. 8 PE m3 5,D J'I2-5-3 v# 0,3 8E D { B, B,, B, B } v 1 2 k n ˆ' P, 8.'1.( T/ f E B >md #,3 v'. U 0m. v6 : * 8E V< 0,3 8E 8 * B i >md #,3 e' f i B k >md f k (i#,r 8E },E &87E., (i#,d,f 8E (c 1 5) v' U. &E P,E v6 ƒ c 1 (3) 8 9 A 1 K F,.,D 8. A.(/ MN. 1/0 0/0 AE 86 v' 9 [19] (2010) { 869 8E. E 5# T,E z T. * 8E f k '[# KF E D k Mb E z /KI3-5-3 F B k >md S 8$3'.*eg 43

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13 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/ /0526 [) -*de 0/0858 1/9099 (4)[) -!.^ $ %&+ > $ )** 0/0238 2/2571 0/5145 2/2807 (6)[) -!.^ $ %,** > $ )** 0/ /4978 0/0091 1/ ' (/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/ ' (/5#,1 [) 8" 2/8908 [) -*de 47

14 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 $3 O F " K,( SN# &?18 H &*3' P FE A,.E L, MN E K,#. A/./ HB,,.E QR3 SN# 0,3 6 6, &FE 8E KNE L,#,i 8E 6 L,. &IE `f K &*6 A#( F " 5E 8 (/[# A 0u# F &86 &1(/[# */ *$) 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, [2] Hadavandi, E., et al., (2011). Tourist arrival forecasting by evolutionary fuzzy systems. Tourism Management, 32(5), [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), [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), [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, [7] Khashei, M., Hejazi, S. R., Bijari, M., (2008). A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. 159, [8] Virtanen, I., Yli-Olli, P., (1990). Working's effect revisited Fitting univariate time series to stock price data. Omega. 18(3), [9] Grillenzoni, C., (1998). Forecasting unstable and nonstationary time series. International Journal of Forecasting, 14(4)

15 ن (١٣٩٢) ٣٧ ٣3-1٩ تورد [10] Yuan, Y., Zhuang, X. T., (2008). Multifractal description of stock price index fluctuation using a quadratic function fitting. Physica A: Statistical Mechanics and its Applications, 387(2-3), [11] Lawrence, R., Using Neural Networks to Forecast Stock Market Price. no. Department of Computer Science, University of Manitoba. [12] Aiken, M. Bsat, M., (1999). Forecasting market trends with neural networks. Information Systems Management, 16(4) [13] Atsalakis, G., Valavanis, K., (2009). Surveying stock market forecasting techniques Part II: Soft computing methods. Expert Systems with Applications, 36, [14] Baba, N., Inoue, N., Asakawa, H., (2000). Utilization of Neural Networks and GAs for Constructing Reliable Decision Support Systems to Deal Stocks. IEEE INNS ENNS International Joint Conference on Neural Networks (IJCNN 00)5, [15] Kuo, R. J., Chenb, C. H., Hwang, Y. C., (2001). An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets and Systems, 118, [16] Asadi, S., et al., (2012). Hybridization of evolutionary Levenberg Marquardt neural networks and data pre-processing for stock market prediction. Knowledge-Based Systems, [17] Doesken, B., et al., (2005). Real stock trading using soft computing models. Proceedings of International Symposium on Information Technology: Coding and Computing ITCC, 2, [18] Chen, Y., et al., (2005). Hybrid methods for stock index modelling, in Proceedings of Fuzzy Systems and Knowledge Discovery. Second International Conference, [19] Yang, X. S., A New Metaheuristic Bat-Inspired Algorithm. Springer, 201, [20] Khan, K., Sahai, A., (2012). A Comparison of BA, GA, PSO, BP and LM for Training Feed forward Neural Networks in e-learning Context. I.J. Intelligent Systems and Applications, 7, [21] Dua, D., Li, K., MinruiFei, (2010). A fastmulti-outputrbfneuralnetworkconstructionmethod. Neurocomputing, 73, [22] Park, J., Sandberg, I. W., (1991). Universal Approximation Using Radial-Basis-Function Networks. Neural Computation, 3, [23] Herman, H., Gudra, T., (2010). New Approach in Bats Sonar Parameterization and Modelling. Physics Procedia, 3,

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