Applications of improved grey prediction model for power demand forecasting


 Benjamin Dickerson
 2 years ago
 Views:
Transcription
1 Energy Conversion and Management 44 (2003) Applications of improved grey prediction model for power demand forecasting CheChiang Hsu a, *, ChiaYon Chen b a Industrial Engineering and Management Department, NanJeon Junior Institute of Technology, 178 ChauChin Road, Yen Shui, Tainan Hisen 73701, Taiwan, ROC b Institute of Resources Engineering, National ChengKung University, 1 TaHsueh Road, Tainan 70101, Taiwan, ROC Received 10 July 2002; accepted 28 October 2002 Abstract Grey theory is a truly multidisciplinary and generic theory that deals with systems that are characterized by poor information and/or for which information is lacking. In this paper, an improved grey GM(1,1) model, using a technique that combines residual modification with artificial neural network sign estimation, is proposed. We use power demand forecasting of Taiwan as our case study to test the efficiency and accuracy of the proposed method. According to the experimental results, our proposed new method obviously can improve the prediction accuracy of the original grey model. Ó 2003 Published by Elsevier Science Ltd. Keywords: Grey theory; Improved GM(1,1) model; Artificial neural network 1. Introduction Grey theory, developed originally by Deng [1], is a truly multidisciplinary and generic theory that deals with systems that are characterized by poor information and/or for which information is lacking. The fields covered by grey theory include systems analysis, data processing, modeling, prediction, decision making and control. The grey theory mainly works on systems analysis with poor, incomplete or uncertain messages. Grey forecasting models have been extensively used in many applications [2 10]. In contrast to statistical methods, the potency of the original series in the time series grey model, called GM(1,1), has been proven to be more than four [11]. In * Corresponding author. Tel.: x62826; fax: address: (C.C. Hsu) /03/$  see front matter Ó 2003 Published by Elsevier Science Ltd. doi: /s (02)
2 2242 C.C. Hsu, C.Y. Chen / Energy Conversion and Management 44 (2003) addition, assumptions regarding the statistical distribution of data are not necessary when applying grey theory. The accumulated generation operation (AGO) is one of the most important characteristics of grey theory, and its main purpose is to reduce the randomness of data. In fact, functions derived from AGO formulations of the original series are always well fitted to exponential functions. In this paper, we introduce a new technique that combines residual modification and residual artificial neural network (ANN) sign estimation to improve the accuracy of the original GM(1,1) model. Furthermore, we use power demand forecasting of Taiwan as our case study to examine the model reliability and accuracy. 2. Original GM(1,1) forecasting model The GM(1,1) is one of the most frequently used grey forecasting model. This model is a time series forecasting model, encompassing a group of differential equations adapted for parameter variance, rather than a first order differential equation. Its difference equations have structures that vary with time rather than being general difference equations. Although it is not necessary to employ all the data from the original series to construct the GM(1,1), the potency of the series must be more than four. In addition, the data must be taken at equal intervals and in consecutive order without bypassing any data [11]. The GM(1,1) model constructing process is described below: Denote the original data sequence by x ð0þ ¼ x ð0þ ð1þ; x ð0þ ð2þ; x ð0þ ð3þ;...; x ð0þ ðnþ ; ð1þ where n is the number of years observed. The AGO formation of x ð0þ is defined as: x ð1þ ¼ x ð1þ ð1þ; x ð1þ ð2þ; x ð1þ ð3þ;...; x ð1þ ðnþ ; ð2þ where x ð1þ ð1þ ¼x ð0þ ð1þ; and x ð1þ ðkþ ¼ Xk m¼1 x ð0þ ðmþ; k ¼ 2; 3;...; n: ð3þ The GM(1,1) model can be constructed by establishing a first order differential equation for x ð1þ ðkþ as: dx ð1þ ðkþ=dk þ ax ð1þ ðkþ ¼b: ð4þ Therefore, the solution of Eq. (4) can be obtained by using the least square method. That is, where ^x ð1þ ðkþ ¼ x ð0þ ð1þ ^b ^a! e ^aðk 1Þ þ ^b ^a ; ð5þ ½^a; ^bš T ¼ðB T BÞ 1 B T X n ð6þ
3 C.C. Hsu, C.Y. Chen / Energy Conversion and Management 44 (2003) and 2 3 0:5ðx ð1þ ð1þþx ð1þ ð2þþ 1 0:5ðx ð1þ ð2þþx ð1þ ð3þþ 1 B ¼ ; ð7þ 0:5ðx ð1þ ðn 1Þþx ð1þ ðnþþ 1 T: X n ¼ x ð0þ ð2þ; x ð0þ ð3þ; x ð0þ ð4þ;...; x ð0þ ðnþ ð8þ We obtained ^x ð1þ from Eq. (5). Let ^x ð0þ be the fitted and predicted series, ^x ð0þ ¼ ^x ð0þ ð1þ; ^x ð0þ ð2þ; ^x ð0þ ð3þ;...; ^x ð0þ ðnþ;... ; ð9þ where ^x ð0þ ð1þ ¼x ð0þ ð1þ. Applying the inverse AGO, we then have ^x ð0þ ðkþ ¼ x ð0þ ð1þ ^b! ð1 e^a Þe ^aðk 1Þ ; k ¼ 2; 3;...; ð10þ ^a where ^x ð0þ ð1þ; ^x ð0þ ð2þ;...; ^x ð0þ ðnþ are called the GM(1,1) fitted sequence, while ^x ð0þ ðn þ 1Þ; ^x ð0þ ðn þ 2Þ;...; are called the GM(1,1) forecast values. 3. Improved grey forecasting model Deng [1] also developed a residual modification model, the residual GM(1,1) model. The differences between the real values, x ð0þ ðkþ, and the model predicted values, ^x ð0þ ðkþ, are defined as the residual series. We denote the residual series as q ð0þ : q ð0þ ¼ q ð0þ ð2þ; q ð0þ ð3þ; q ð0þ ð4þ;...; q ð0þ ðnþ ; ð11þ where q ð0þ ðkþ ¼x ð0þ ðkþ ^x ð0þ ðkþ: The residual GM(1,1) model could be established to improve the predictive accuracy of the original GM(1,1) model. The modified prediction values can be obtained by adding the forecasted values of the residual GM(1,1) model to the original ^x ð0þ ðkþ. However, the potency of the residual series depends on the number of data points with the same sign, which is usually small when there are few observations. In these cases, the potency of the residual series with the same sign may not be more than four, and a residual GM(1,1) model cannot be established. Here, we present an improved grey model to solve this problem. We establish a modification submodel that is a combination residual GM(1,1) forecaster that uses the absolute values of the residual series with an ANN for residual sign estimation. The schematic of the improved forecasting system is shown in Fig. 1. The detail process to formulate this improved grey forecast model is described as follows. ð12þ
4 2244 C.C. Hsu, C.Y. Chen / Energy Conversion and Management 44 (2003) Data Input Original Model Original GM(1,1) Forecaster Original Forecast Output Modification SubModel Residual Input Residual GM(1,1) Forecaster Residual Forecast Output Combination Module Final Forecast ANN Sign Estimater Residual Sign Eastimate Fig. 1. Schematic of the forecasting system Residual forecasting model First, denote the absolute values of the residual series as e ð0þ : e ð0þ ¼ e ð0þ ð2þ; e ð0þ ð3þ; e ð0þ ð4þ;...; e ð0þ ðnþ ; ð13þ where e ð0þ ¼ q ð0þ ðkþ ; k ¼ 2; 3;...; n: ð14þ By using the same methods as Eqs. (1) (10), a GM(1,1) model of e ð0þ can be established. Denote the forecast residual series as ^e ð0þ ðkþ, then ^e ð0þ ðkþ ¼ e ð0þ ð2þ b e ð1 e a e Þe aeðk 1Þ ; k ¼ 2; 3;... ð15þ a e 3.2. ANN residual sign estimation model In recent years, much research has been conducted on the application of artificial intelligence techniques to forecasting problems. However, the model that has received extensive attention is undoubtedly the ANN, cited as among the most powerful computational tools ever developed. Fig. 2 presents an outline of a simple biological neural and an ANNÕs basic elements. ANN models operate like a black box, requiring no detailed information about the system. Instead, they learn the relationship between the input parameters and the controlled and uncontrolled variables by studying previous data. ANN models could handle large and complex systems with many interrelated parameters. Several types of neural architectures are available, among which the multilayer back propagation (BP) neural network is the most widely used. As Fig. 3 reveals, a BP network typically employs three or more layers for the architecture: an input layer, an output layer and at least one hidden layer. The computational procedure of this network is described below: Y j ¼ f X W ij X ij!; ð16þ i
5 C.C. Hsu, C.Y. Chen / Energy Conversion and Management 44 (2003) Fig. 2. A simple neural [13] vs. a PE model. where Y j is the output of node j, f ðþ is the transfer function, w ij is the connection weight between node j and node i in the lower layer and X i is the input signal from the node i in the lower layer. BP is a gradient descent algorithm. It tries to improve the performance of the neural network by reducing the total error by changing the weights along its gradient. The BP algorithm minimizes the square errors, which can be calculated by: Fig. 3. A BP network.
6 2246 C.C. Hsu, C.Y. Chen / Energy Conversion and Management 44 (2003) d(n1) d(n) Input Layer Hidden Layer Output Layer d(n+1) Bias Fig. 4. Structure of ANN sign forecasting system. E ¼ 1=2 X X ½O p j Y p j Š 2 ; ð17þ p j where E is the square errors, p is the index of the pattern, O is the actual (target) output and Y is the network output. A two state ANN model is used here to predict the signs of the forecast residual series. First, we introduce a dummy variable dðkþ to indicate the sign of the kth year residual. Assume the sign of the kth year residual is positive, then the value of dðkþ is 1, otherwise it is 0. Then, we set up an ANN model by using the values of dðn 1Þ and dðnþ to estimate the values of dðn þ 1Þ. The structure of this ANN sign forecasting system is shown in Fig. 4. Let the sign of the kth year residual, sðkþ, be þ1; if dðkþ ¼1 sðkþ ¼ ; k ¼ 1; 2;...; n;... ð18þ 1; if dðkþ ¼0 According to the equations illustrated above, an improved grey model combination residual modification with ANN sign estimation can be further formulated as Eq. (19) ^x 0ð0Þ ðkþ ¼ x ð0þ ð1þ b ð1 e a Þe aðk 1Þ þ sðkþ e ð0þ ð2þ b e ð1 e a e Þe aeðk 1Þ ; a a e k ¼ 1; 2;...; n; n þ 1;... ð19þ Next, we will proceed to the power demand forecasting of Taiwan for our case study to examine the reliability and accuracy of this improved GM(1,1) model. 4. Results To demonstrate the effectiveness of the proposed method, we use the power demand forecasting of Taiwan as an illustrating example. In this study, we use the historical annual power demand of Taiwan from 1985 to 2000 as our research data. There are 16 observations, where are used for model fitting and are reserved for ex post testing. For the purposes of comparison, we also use the same number of observations, 14 (power demand from 1985 to 1998), to formulate an ARIMA (p; d; q) model, where p is the order of the autoregressive part, d is the order of the differencing, and q is the order of the moving average
7 Table 1 Model values and forecast errors (unit: 10 3 Wh) Year Real value GM(1,1) Improved GM(1,1) ARIMA Model value Error (%) Model value Error (%) Model value Error (%) ,919,102 47,919, ,919, ,919, ,812,862 56,318, ,812, ,307,500 ) ,174,751 60,319, ,630, ,957,006 ) ,227,727 64,605,914 ) ,310, ,243,936 ) ,251,809 69,196,550 ) ,917, ,958,706 ) ,344,947 74,113,379 ) ,850, ,405,080 ) ,977,405 79,379,577 ) ,133,358 ) ,688,020 ) ,290,354 85,019,971 ) ,790, ,105,943 ) ,084,684 91,061,148 ) ,849,611 ) ,156,925 ) ,561,004 97,531,587 ) ,337,985 ) ,739,526 ) ,368, ,461,790 ) ,286,530 ) ,954,923 ) ,139, ,884, ,040,924 ) ,828,630 ) ,299, ,834, ,971, ,049,615 ) ,129, ,349, ,467,127 ) ,169,150 )5.43 MAPE a ( ) C.C. Hsu, C.Y. Chen / Energy Conversion and Management 44 (2003) ,725, ,469, ,459, ,756,418 ) ,412, ,237, ,204, ,168,992 )2.28 MAPE ( ) a P MAPE ¼ 1 n n k¼1 ½j^x ð0þ ðkþ x ð0þ ðkþj=x ð0þ ðkþš Fig. 5. Real values and model values for power demand of Taiwan from 1985 to 2000.
8 2248 C.C. Hsu, C.Y. Chen / Energy Conversion and Management 44 (2003) Fig. 6. Model percentage error distribution from 1985 to process [12]. As a result of statistical tests, the ARIMA model with ðp; d; qþ ¼ð0; 1; 0Þ is formulated as follows: ^xðkþ ¼ :67 þ 1:04^xðk 1Þ; k ¼ 2; 3; 4;...; n;... ð20þ The predicted results obtained by the original GM(1,1) model, improved GM(1,1) model and ARIMA model are shown in Table 1 and Fig. 5. The model percentage error distribution is also shown in Fig. 6. The mean absolute percentage error (MAPE) of the GM(1,1) model, the ARIMA model and our improved GM(1,1) model from 1999 to 2000 are 3.88%, 2.27% and 1.29%, respectively. According to the results shown above, our improved grey model seems to obtain the lowest postforecasting errors among these models. It is indicated that the modification of our improved GM(1,1) model can reduce model prediction errors effectively. 5. Conclusions The original GM(1,1) model is a model with a group of differential equations adapted for variance of parameters, and it is a powerful forecasting model, especially when the number of observations is not large. In this paper, we have applied an improved grey GM(1,1) model by using a technique that combines residual modification with ANN sign estimations. Our study results show that this method can yield more accurate results than the original GM(1,1) model and also solve problems resulting from having too few data, which may lead the same sign residuals lower than four and violate the necessary condition of setting up a GM(1,1) model. The improved grey models were then applied to predict the power demand of Taiwan. Finally, through this study, our improved grey model, in this paper, is an appropriate forecasting method to yield more accurate results than the original GM(1,1) model.
9 C.C. Hsu, C.Y. Chen / Energy Conversion and Management 44 (2003) References [1] Deng JL. Grey system fundamental method. Huazhong University of Science and Technology Wuhan, China, [2] Sun G. Prediction of vegetable yields by grey model GM(1,1). JGrey Syst 1991;2: [3] Morita H et al. Interval prediction of annual maximum demand using grey dynamic model. Electr Power Energy Syst 1996;18: [4] Huang YP, Wang SF. The identification of fuzzy grey prediction system by genetic algorithm. Int JSyst Sci 1997;28: [5] Hsu CI, Wen YH. Improved grey prediction models for the TransPacific air passenger market. Transport Planning Technol 1998;22: [6] Hsu CC, Chen CY. Application of grey theory to regional electricity demand forecasting. Energy Quart 1999;24: [7] Hao YH, Wang XM. The study of grey system models of Niangziguan spring. JSyst Eng 2000;16: [8] Liu SF, Deng JL. The range suitable for GM(1,1). Syst Eng Theor Appl 2000;5: [9] Xing M. Research on combined grey neural network model of seasonal forecast. Syst Eng Theor Appl 2001;11: [10] Yue CL, Wang L. Grey Markov forecast of the stock price. Syst Eng 2000;16:54 9. [11] Deng JL. Grey prediction and decision. Huazhong University of Science and Technology, Wuhan, China, [12] Box GEP, Jenkins GM, Reinsel GC. Time series analysis: forecasting and control. NJ: Prentice Hall; [13] Nelson MM, Illingworth WT. A practical guide to neveal nets. MA: AddisonWesley; 1993.
Energy Conversion and Management
Energy Conversion and Management 52 (2011) 147 152 Contents lists available at ScienceDirect Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman Forecasting energy consumption
More informationA Multilevel Artificial Neural Network for Residential and Commercial Energy Demand Forecast: Iran Case Study
211 3rd International Conference on Information and Financial Engineering IPEDR vol.12 (211) (211) IACSIT Press, Singapore A Multilevel Artificial Neural Network for Residential and Commercial Energy
More informationSupply Chain Forecasting Model Using Computational Intelligence Techniques
CMU.J.Nat.Sci Special Issue on Manufacturing Technology (2011) Vol.10(1) 19 Supply Chain Forecasting Model Using Computational Intelligence Techniques Wimalin S. Laosiritaworn Department of Industrial
More informationChapter 2 The Research on Fault Diagnosis of Building Electrical System Based on RBF Neural Network
Chapter 2 The Research on Fault Diagnosis of Building Electrical System Based on RBF Neural Network Qian Wu, Yahui Wang, Long Zhang and Li Shen Abstract Building electrical system fault diagnosis is the
More informationNTC Project: S01PH10 (formerly I01P10) 1 Forecasting Women s Apparel Sales Using Mathematical Modeling
1 Forecasting Women s Apparel Sales Using Mathematical Modeling Celia Frank* 1, Balaji Vemulapalli 1, Les M. Sztandera 2, Amar Raheja 3 1 School of Textiles and Materials Technology 2 Computer Information
More informationDesign call center management system of ecommerce based on BP neural network and multifractal
Available online www.jocpr.com Journal of Chemical and Pharmaceutical Research, 2014, 6(6):951956 Research Article ISSN : 09757384 CODEN(USA) : JCPRC5 Design call center management system of ecommerce
More informationOpen Access Research on Application of Neural Network in Computer Network Security Evaluation. Shujuan Jin *
Send Orders for Reprints to reprints@benthamscience.ae 766 The Open Electrical & Electronic Engineering Journal, 2014, 8, 766771 Open Access Research on Application of Neural Network in Computer Network
More informationDemand Forecasting and Production Planning for Highly Seasonal Demand Situations: Case Study of a Pressure Container Factory
ScienceAsia 27 (2) : 27278 Demand Forecasting and Production Planning for Highly Seasonal Demand Situations: Case Study of a Pressure Container Factory Pisal Yenradee a,*, Anulark Pinnoi b and Amnaj Charoenthavornying
More informationA New Method for Traffic Forecasting Based on the Data Mining Technology with Artificial Intelligent Algorithms
Research Journal of Applied Sciences, Engineering and Technology 5(12): 34173422, 213 ISSN: 247459; eissn: 247467 Maxwell Scientific Organization, 213 Submitted: October 17, 212 Accepted: November
More informationDemand Forecasting Optimization in Supply Chain
2011 International Conference on Information Management and Engineering (ICIME 2011) IPCSIT vol. 52 (2012) (2012) IACSIT Press, Singapore DOI: 10.7763/IPCSIT.2012.V52.12 Demand Forecasting Optimization
More informationPower Prediction Analysis using Artificial Neural Network in MS Excel
Power Prediction Analysis using Artificial Neural Network in MS Excel NURHASHINMAH MAHAMAD, MUHAMAD KAMAL B. MOHAMMED AMIN Electronic System Engineering Department Malaysia Japan International Institute
More informationFuzzy regression model with fuzzy input and output data for manpower forecasting
Fuzzy Sets and Systems 9 (200) 205 23 www.elsevier.com/locate/fss Fuzzy regression model with fuzzy input and output data for manpower forecasting Hong Tau Lee, Sheu Hua Chen Department of Industrial Engineering
More informationStudying Achievement
Journal of Business and Economics, ISSN 21557950, USA November 2014, Volume 5, No. 11, pp. 20522056 DOI: 10.15341/jbe(21557950)/11.05.2014/009 Academic Star Publishing Company, 2014 http://www.academicstar.us
More informationComputational Neural Network for Global Stock Indexes Prediction
Computational Neural Network for Global Stock Indexes Prediction Dr. Wilton.W.T. Fok, IAENG, Vincent.W.L. Tam, Hon Ng Abstract  In this paper, computational data mining methodology was used to predict
More informationHandling of incomplete data sets using ICA and SOM in data mining
Neural Comput & Applic (2007) 16: 167 172 DOI 10.1007/s0052100600586 ORIGINAL ARTICLE Hongyi Peng Æ Siming Zhu Handling of incomplete data sets using ICA and SOM in data mining Received: 2 September
More informationForecasting of Economic Quantities using Fuzzy Autoregressive Model and Fuzzy Neural Network
Forecasting of Economic Quantities using Fuzzy Autoregressive Model and Fuzzy Neural Network Dušan Marček 1 Abstract Most models for the time series of stock prices have centered on autoregressive (AR)
More information3 Results. σdx. df =[µ 1 2 σ 2 ]dt+ σdx. Integration both sides will form
Appl. Math. Inf. Sci. 8, No. 1, 107112 (2014) 107 Applied Mathematics & Information Sciences An International Journal http://dx.doi.org/10.12785/amis/080112 Forecasting Share Prices of Small Size Companies
More informationIranian J Env Health Sci Eng, 2004, Vol.1, No.2, pp.5157. Application of Intelligent System for Water Treatment Plant Operation.
Iranian J Env Health Sci Eng, 2004, Vol.1, No.2, pp.5157 Application of Intelligent System for Water Treatment Plant Operation *A Mirsepassi Dept. of Environmental Health Engineering, School of Public
More informationA Prediction Model for Taiwan Tourism Industry Stock Index
A Prediction Model for Taiwan Tourism Industry Stock Index ABSTRACT HanChen Huang and FangWei Chang Yu Da University of Science and Technology, Taiwan Investors and scholars pay continuous attention
More informationForecasting of Paddy Production in Sri Lanka: A Time Series Analysis using ARIMA Model
Tropical Agricultural Research Vol. 24 (): 23 (22) Forecasting of Paddy Production in Sri Lanka: A Time Series Analysis using ARIMA Model V. Sivapathasundaram * and C. Bogahawatte Postgraduate Institute
More informationTIME SERIES FORECASTING WITH NEURAL NETWORK: A CASE STUDY OF STOCK PRICES OF INTERCONTINENTAL BANK NIGERIA
www.arpapress.com/volumes/vol9issue3/ijrras_9_3_16.pdf TIME SERIES FORECASTING WITH NEURAL NETWORK: A CASE STUDY OF STOCK PRICES OF INTERCONTINENTAL BANK NIGERIA 1 Akintola K.G., 2 Alese B.K. & 2 Thompson
More informationSINGULAR SPECTRUM ANALYSIS HYBRID FORECASTING METHODS WITH APPLICATION TO AIR TRANSPORT DEMAND
SINGULAR SPECTRUM ANALYSIS HYBRID FORECASTING METHODS WITH APPLICATION TO AIR TRANSPORT DEMAND K. Adjenughwure, Delft University of Technology, Transport Institute, Ph.D. candidate V. Balopoulos, Democritus
More informationPrice Prediction of Share Market using Artificial Neural Network (ANN)
Prediction of Share Market using Artificial Neural Network (ANN) Zabir Haider Khan Department of CSE, SUST, Sylhet, Bangladesh Tasnim Sharmin Alin Department of CSE, SUST, Sylhet, Bangladesh Md. Akter
More informationBank Customers (Credit) Rating System Based On Expert System and ANN
Bank Customers (Credit) Rating System Based On Expert System and ANN Project Review Yingzhen Li Abstract The precise rating of customers has a decisive impact on loan business. We constructed the BP network,
More informationA Forecasting Decision Support System
A Forecasting Decision Support System Hanaa E.Sayed a, *, Hossam A.Gabbar b, Soheir A. Fouad c, Khalil M. Ahmed c, Shigeji Miyazaki a a Department of Systems Engineering, Division of Industrial Innovation
More informationLecture 6. Artificial Neural Networks
Lecture 6 Artificial Neural Networks 1 1 Artificial Neural Networks In this note we provide an overview of the key concepts that have led to the emergence of Artificial Neural Networks as a major paradigm
More informationSales Forecast for Pickup Truck Parts:
Sales Forecast for Pickup Truck Parts: A Case Study on Brake Rubber Mojtaba Kamranfard University of Semnan Semnan, Iran mojtabakamranfard@gmail.com Kourosh Kiani Amirkabir University of Technology Tehran,
More informationHorse Racing Prediction Using Artificial Neural Networks
Horse Racing Prediction Using Artificial Neural Networks ELNAZ DAVOODI, ALI REZA KHANTEYMOORI Mathematics and Computer science Department Institute for Advanced Studies in Basic Sciences (IASBS) Gavazang,
More informationNeural Network Based Forecasting of Foreign Currency Exchange Rates
Neural Network Based Forecasting of Foreign Currency Exchange Rates S. Kumar Chandar, PhD Scholar, Madurai Kamaraj University, Madurai, India kcresearch2014@gmail.com Dr. M. Sumathi, Associate Professor,
More informationA Service Revenueoriented Task Scheduling Model of Cloud Computing
Journal of Information & Computational Science 10:10 (2013) 3153 3161 July 1, 2013 Available at http://www.joics.com A Service Revenueoriented Task Scheduling Model of Cloud Computing Jianguang Deng a,b,,
More informationFuzzy Delphi and backpropagation model for sales forecasting in PCB industry
Expert Systems with Applications 30 (2006) 715 726 www.elsevier.com/locate/eswa Fuzzy Delphi and backpropagation model for sales forecasting in PCB industry PeiChann Chang a, *, YenWen Wang b a Department
More informationA Comparative Study of the Pickup Method and its Variations Using a Simulated Hotel Reservation Data
A Comparative Study of the Pickup Method and its Variations Using a Simulated Hotel Reservation Data Athanasius Zakhary, Neamat El Gayar Faculty of Computers and Information Cairo University, Giza, Egypt
More informationDiagnosis of multioperational machining processes through variation propagation analysis
Robotics and Computer Integrated Manufacturing 18 (2002) 233 239 Diagnosis of multioperational machining processes through variation propagation analysis Qiang Huang, Shiyu Zhou, Jianjun Shi* Department
More informationSoftware Development Cost and Time Forecasting Using a High Performance Artificial Neural Network Model
Software Development Cost and Time Forecasting Using a High Performance Artificial Neural Network Model Iman Attarzadeh and Siew Hock Ow Department of Software Engineering Faculty of Computer Science &
More informationANN Model to Predict Stock Prices at Stock Exchange Markets
ANN Model to Predict Stock Prices at Stock Exchange Markets Wanjawa, Barack Wamkaya School of Computing and Informatics, University of Nairobi, wanjawawb@gmail.com Muchemi, Lawrence School of Computing
More informationNeural NetworkBased Tool Breakage Monitoring System for End Milling Operations
Journal of Industrial Technology Volume 6, Number 2 Februrary 2000 to April 2000 www.nait.org Volume 6, Number 2  February 2000 to April 2000 Neural NetworkBased Tool Breakage Monitoring System for End
More informationJoseph Twagilimana, University of Louisville, Louisville, KY
ST14 Comparing Time series, Generalized Linear Models and Artificial Neural Network Models for Transactional Data analysis Joseph Twagilimana, University of Louisville, Louisville, KY ABSTRACT The aim
More informationA comparison between the grey molding and neural network in. the prediction of the lubrication oil comsumption of linear motion.
A comparison between the grey molding and neural networ in the prediction of the lubrication oil comsumption of linear motion Y.F. Hsiao guide, Y.S. Tarng 1 2 1 Department of Mechanical Engineering Army
More informationAnalysis of China Motor Vehicle Insurance Business Trends
Analysis of China Motor Vehicle Insurance Business Trends 1 Xiaohui WU, 2 Zheng Zhang, 3 Lei Liu, 4 Lanlan Zhang 1, First Autho University of International Business and Economic, Beijing, wuxiaohui@iachina.cn
More informationFlexible Neural Trees Ensemble for Stock Index Modeling
Flexible Neural Trees Ensemble for Stock Index Modeling Yuehui Chen 1, Ju Yang 1, Bo Yang 1 and Ajith Abraham 2 1 School of Information Science and Engineering Jinan University, Jinan 250022, P.R.China
More informationThresholding technique with adaptive window selection for uneven lighting image
Pattern Recognition Letters 26 (2005) 801 808 wwwelseviercom/locate/patrec Thresholding technique with adaptive window selection for uneven lighting image Qingming Huang a, *, Wen Gao a, Wenjian Cai b
More informationThe Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network
, pp.6776 http://dx.doi.org/10.14257/ijdta.2016.9.1.06 The Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network Lihua Yang and Baolin Li* School of Economics and
More informationStock Data Analysis Based On Neural Network. 1Rajesh Musne, 2 Sachin Godse
Stock Analysis Based On Neural Network. 1Rajesh Musne, 2 Sachin Godse 1ME Research Scholar Department of Computer Engineering 2 Assistant Professor Department of Computer Engineering Sinhgad Academy Of
More informationForecasting Stock Prices using a Weightless Neural Network. Nontokozo Mpofu
Forecasting Stock Prices using a Weightless Neural Network Nontokozo Mpofu Abstract In this research work, we propose forecasting stock prices in the stock market industry in Zimbabwe using a Weightless
More informationOptimization of PID parameters with an improved simplex PSO
Li et al. Journal of Inequalities and Applications (2015) 2015:325 DOI 10.1186/s1366001507852 R E S E A R C H Open Access Optimization of PID parameters with an improved simplex PSO Jimin Li 1, YeongCheng
More informationPerformance Evaluation and Prediction of ITOutsourcing Service Supply Chain based on Improved SCOR Model
Performance Evaluation and Prediction of ITOutsourcing Service Supply Chain based on Improved SCOR Model 1, 2 1 International School of Software, Wuhan University, Wuhan, China *2 School of Information
More informationForecasting areas and production of rice in India using ARIMA model
International Journal of Farm Sciences 4(1) :99106, 2014 Forecasting areas and production of rice in India using ARIMA model K PRABAKARAN and C SIVAPRAGASAM* Agricultural College and Research Institute,
More informationStudy on fruit quality measurement and evaluation based on color identification. Wang, Y; Cui, Y; Chen, S; Zhang, P; Huang, H; Huang, GQ
Title Study on fruit quality measurement and evaluation based on color identification Author(s) Wang, Y; Cui, Y; Chen, S; Zhang, P; Huang, H; Huang, GQ Citation International Conference on Optical Instruments
More informationA New Method for Electric Consumption Forecasting in a Semiconductor Plant
A New Method for Electric Consumption Forecasting in a Semiconductor Plant Prayad Boonkham 1, Somsak Surapatpichai 2 Spansion Thailand Limited 229 Moo 4, Changwattana Road, Pakkred, Nonthaburi 11120 Nonthaburi,
More informationCOMBINED NEURAL NETWORKS FOR TIME SERIES ANALYSIS
COMBINED NEURAL NETWORKS FOR TIME SERIES ANALYSIS Iris Ginzburg and David Horn School of Physics and Astronomy Raymond and Beverly Sackler Faculty of Exact Science TelAviv University TelA viv 96678,
More informationThe development of a weighted evolving fuzzy neural network for PCB sales forecasting
Expert Systems with Applications Expert Systems with Applications 32 (2007) 86 96 www.elsevier.com/locate/eswa The development of a weighted evolving fuzzy neural network for PCB sales forecasting PeiChann
More informationA study of longterm climatology of ionospheric irregularities by using GPS phase fluctuations at the Brazilian longitudes
Advances in Space Research xxx (2007) xxx xxx www.elsevier.com/locate/asr A study of longterm climatology of ionospheric irregularities by using GPS phase fluctuations at the Brazilian longitudes F.D.
More informationImpact of Feature Selection on the Performance of Wireless Intrusion Detection Systems
2009 International Conference on Computer Engineering and Applications IPCSIT vol.2 (2011) (2011) IACSIT Press, Singapore Impact of Feature Selection on the Performance of ireless Intrusion Detection Systems
More informationEvaluation of Feature Selection Methods for Predictive Modeling Using Neural Networks in Credits Scoring
714 Evaluation of Feature election Methods for Predictive Modeling Using Neural Networks in Credits coring Raghavendra B. K. Dr. M.G.R. Educational and Research Institute, Chennai95 Email: raghavendra_bk@rediffmail.com
More informationA New Approach to Neural Network based Stock Trading Strategy
A New Approach to Neural Network based Stock Trading Strategy Miroslaw Kordos, Andrzej Cwiok University of BielskoBiala, Department of Mathematics and Computer Science, BielskoBiala, Willowa 2, Poland:
More informationAPPLYING DATA MINING TECHNIQUES TO FORECAST NUMBER OF AIRLINE PASSENGERS
APPLYING DATA MINING TECHNIQUES TO FORECAST NUMBER OF AIRLINE PASSENGERS IN SAUDI ARABIA (DOMESTIC AND INTERNATIONAL TRAVELS) Abdullah Omer BaFail King Abdul Aziz University Jeddah, Saudi Arabia ABSTRACT
More informationNTC Project: S01PH10 (formerly I01P10) 1 Forecasting Women s Apparel Sales Using Mathematical Modeling
1 Forecasting Women s Apparel Sales Using Mathematical Modeling Celia Frank* 1, Balaji Vemulapalli 1, Les M. Sztandera 2, Amar Raheja 3 1 School of Textiles and Materials Technology 2 Computer Information
More informationDesigning a neural network for forecasting financial time series
Designing a neural network for forecasting financial time series 29 février 2008 What a Neural Network is? Each neurone k is characterized by a transfer function f k : output k = f k ( i w ik x k ) From
More informationComparison of Kmeans and Backpropagation Data Mining Algorithms
Comparison of Kmeans and Backpropagation Data Mining Algorithms Nitu Mathuriya, Dr. Ashish Bansal Abstract Data mining has got more and more mature as a field of basic research in computer science and
More informationForecasting Of Indian Stock Market Index Using Artificial Neural Network
Forecasting Of Indian Stock Market Index Using Artificial Neural Network Proposal Page 1 of 8 ABSTRACT The objective of the study is to present the use of artificial neural network as a forecasting tool
More informationAn Evaluation Model for Determining Insurance Policy Using AHP and Fuzzy Logic: Case Studies of Life and Annuity Insurances
Proceedings of the 8th WSEAS International Conference on Fuzzy Systems, Vancouver, British Columbia, Canada, June 1921, 2007 126 An Evaluation Model for Determining Insurance Policy Using AHP and Fuzzy
More informationCredit Card Fraud Detection Using Self Organised Map
International Journal of Information & Computation Technology. ISSN 09742239 Volume 4, Number 13 (2014), pp. 13431348 International Research Publications House http://www. irphouse.com Credit Card Fraud
More informationTRAINING A LIMITEDINTERCONNECT, SYNTHETIC NEURAL IC
777 TRAINING A LIMITEDINTERCONNECT, SYNTHETIC NEURAL IC M.R. Walker. S. Haghighi. A. Afghan. and L.A. Akers Center for Solid State Electronics Research Arizona State University Tempe. AZ 852876206 mwalker@enuxha.eas.asu.edu
More informationA comprehensive framework for selecting an ERP system
International Journal of Project Management 22 (2004) 161 169 www.elsevier.com/locate/ijproman A comprehensive framework for selecting an ERP system ChunChin Wei, MaoJiun J. Wang* Department of Industrial
More informationNeural Network Applications in Stock Market Predictions  A Methodology Analysis
Neural Network Applications in Stock Market Predictions  A Methodology Analysis Marijana Zekic, MS University of Josip Juraj Strossmayer in Osijek Faculty of Economics Osijek Gajev trg 7, 31000 Osijek
More informationChapter 2 Laser Diode Beam Propagation Basics
Chapter 2 Laser Diode Beam Propagation Basics Abstract Laser diode beam propagation characteristics, the collimating and focusing behaviors and the M 2 factor are discussed using equations and graphs.
More informationIBM SPSS Forecasting 22
IBM SPSS Forecasting 22 Note Before using this information and the product it supports, read the information in Notices on page 33. Product Information This edition applies to version 22, release 0, modification
More informationA hybrid financial analysis model for business failure prediction
Available online at www.sciencedirect.com Expert Systems with Applications Expert Systems with Applications 35 (2008) 1034 1040 www.elsevier.com/locate/eswa A hybrid financial analysis model for business
More informationTOURISM DEMAND FORECASTING USING A NOVEL HIGHPRECISION FUZZY TIME SERIES MODEL. RueyChyn Tsaur and TingChun Kuo
International Journal of Innovative Computing, Information and Control ICIC International c 2014 ISSN 13494198 Volume 10, Number 2, April 2014 pp. 695 701 OURISM DEMAND FORECASING USING A NOVEL HIGHPRECISION
More informationPerformance Evaluation of Artificial Neural. Networks for Spatial Data Analysis
Contemporary Engineering Sciences, Vol. 4, 2011, no. 4, 149163 Performance Evaluation of Artificial Neural Networks for Spatial Data Analysis Akram A. Moustafa Department of Computer Science Al albayt
More informationTime Series Data Mining in Rainfall Forecasting Using Artificial Neural Network
Time Series Data Mining in Rainfall Forecasting Using Artificial Neural Network Prince Gupta 1, Satanand Mishra 2, S.K.Pandey 3 1,3 VNS Group, RGPV, Bhopal, 2 CSIRAMPRI, BHOPAL prince2010.gupta@gmail.com
More informationData Mining using Artificial Neural Network Rules
Data Mining using Artificial Neural Network Rules Pushkar Shinde MCOERC, Nasik Abstract  Diabetes patients are increasing in number so it is necessary to predict, treat and diagnose the disease. Data
More informationFOREX TRADING PREDICTION USING LINEAR REGRESSION LINE, ARTIFICIAL NEURAL NETWORK AND DYNAMIC TIME WARPING ALGORITHMS
FOREX TRADING PREDICTION USING LINEAR REGRESSION LINE, ARTIFICIAL NEURAL NETWORK AND DYNAMIC TIME WARPING ALGORITHMS Leslie C.O. Tiong 1, David C.L. Ngo 2, and Yunli Lee 3 1 Sunway University, Malaysia,
More informationTime Series and Forecasting
Chapter 22 Page 1 Time Series and Forecasting A time series is a sequence of observations of a random variable. Hence, it is a stochastic process. Examples include the monthly demand for a product, the
More informationFeedforward Neural Networks and Backpropagation
Feedforward Neural Networks and Backpropagation Feedforward neural networks Architectural issues, computational capabilities Sigmoidal and radial basis functions Gradientbased learning and Backprogation
More informationTime Series Analysis
JUNE 2012 Time Series Analysis CONTENT A time series is a chronological sequence of observations on a particular variable. Usually the observations are taken at regular intervals (days, months, years),
More informationInternational Journal of Asian Social Science VIETNAMESE MILK INDUSTRY FORECASTING: A GREY SYSTEM THEORY CASE OF VINAMILK.
International Journal of Asian Social Science ISSN(e): 22244441/ISSN(p): 22265139 journal homepage: http://www.aessweb.com/journals/5007 VIETNAMESE MILK INDUSTRY FORECASTING: A GREY SYSTEM THEORY CASE
More informationA Data Mining Study of Weld Quality Models Constructed with MLP Neural Networks from Stratified Sampled Data
A Data Mining Study of Weld Quality Models Constructed with MLP Neural Networks from Stratified Sampled Data T. W. Liao, G. Wang, and E. Triantaphyllou Department of Industrial and Manufacturing Systems
More informationFollow links Class Use and other Permissions. For more information, send email to: permissions@pupress.princeton.edu
COPYRIGHT NOTICE: David A. Kendrick, P. Ruben Mercado, and Hans M. Amman: Computational Economics is published by Princeton University Press and copyrighted, 2006, by Princeton University Press. All rights
More informationADVANCED FORECASTING MODELS USING SAS SOFTWARE
ADVANCED FORECASTING MODELS USING SAS SOFTWARE Girish Kumar Jha IARI, Pusa, New Delhi 110 012 gjha_eco@iari.res.in 1. Transfer Function Model Univariate ARIMA models are useful for analysis and forecasting
More informationPredicting performance measures using linear regression and neural network: A comparison
African Journal of Engineering Research Vol. 1(3), pp. 8489, July 2013 Full Length Research Paper Predicting performance measures using linear regression and neural network: A comparison Anyaeche C. O.*
More informationResearch on the Comprehensive Evaluation of Business Intelligence System Based on BP Neural Network
Available online at www.sciencedirect.com Systems Engineering Procedia 00 (2011) 000 000 Systems Engineering Procedia 4 (2012) 275 281 Systems Engineering Procedia www.elsevier.com/locate/procedia 2 nd
More informationBrand management model of vocational high schools in Taiwan
Available online at www.sciencedirect.com Procedia Social and Behavioral Sciences 2 (2010) 4229 4233 WCES2010 management model of vocational high schools in Taiwan YiLing Hung a, ChingSheue Fu a * a
More informationA Control Method of Traffic Flow Based on Region Coordination
3rd International Conference on Management, Education, Information and Control (MEICI 2015) A Control Method of Traffic Flow Based on Region Coordination Wuxiong Xu 1, a,*, Dong Zhong 1, b, Siqing Wu 1,
More informationA Time Series ANN Approach for Weather Forecasting
A Time Series ANN Approach for Weather Forecasting Neeraj Kumar 1, Govind Kumar Jha 2 1 Associate Professor and Head Deptt. Of Computer Science,Nalanda College Of Engineering Chandi(Bihar) 2 Assistant
More informationPerformance Evaluation On Human Resource Management Of China S Commercial Banks Based On Improved Bp Neural Networks
Performance Evaluation On Human Resource Management Of China S *1 Honglei Zhang, 2 Wenshan Yuan, 1 Hua Jiang 1 School of Economics and Management, Hebei University of Engineering, Handan 056038, P. R.
More informationPortfolio selection based on upper and lower exponential possibility distributions
European Journal of Operational Research 114 (1999) 115±126 Theory and Methodology Portfolio selection based on upper and lower exponential possibility distributions Hideo Tanaka *, Peijun Guo Department
More informationDynamic intelligent cleaning model of dirty electric load data
Available online at www.sciencedirect.com Energy Conversion and Management 49 (2008) 564 569 www.elsevier.com/locate/enconman Dynamic intelligent cleaning model of dirty electric load data Zhang Xiaoxing
More informationA New Approach For Estimating Software Effort Using RBFN Network
IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.7, July 008 37 A New Approach For Estimating Software Using RBFN Network Ch. Satyananda Reddy, P. Sankara Rao, KVSVN Raju,
More informationTIME SERIES ANALYSIS
TIME SERIES ANALYSIS Ramasubramanian V. I.A.S.R.I., Library Avenue, New Delhi 110 012 ram_stat@yahoo.co.in 1. Introduction A Time Series (TS) is a sequence of observations ordered in time. Mostly these
More informationEffect of Using Neural Networks in GABased School Timetabling
Effect of Using Neural Networks in GABased School Timetabling JANIS ZUTERS Department of Computer Science University of Latvia Raina bulv. 19, Riga, LV1050 LATVIA janis.zuters@lu.lv Abstract:  The school
More informationPerformance Based Evaluation of New Software Testing Using Artificial Neural Network
Performance Based Evaluation of New Software Testing Using Artificial Neural Network Jogi John 1, Mangesh Wanjari 2 1 Priyadarshini College of Engineering, Nagpur, Maharashtra, India 2 Shri Ramdeobaba
More informationTime Series Analysis and Forecasting Methods for Temporal Mining of Interlinked Documents
Time Series Analysis and Forecasting Methods for Temporal Mining of Interlinked Documents Prasanna Desikan and Jaideep Srivastava Department of Computer Science University of Minnesota. @cs.umn.edu
More informationNeural Network Design in Cloud Computing
International Journal of Computer Trends and Technology volume4issue22013 ABSTRACT: Neural Network Design in Cloud Computing B.Rajkumar #1,T.Gopikiran #2,S.Satyanarayana *3 #1,#2Department of Computer
More informationEFFICIENT DATA PREPROCESSING FOR DATA MINING
EFFICIENT DATA PREPROCESSING FOR DATA MINING USING NEURAL NETWORKS JothiKumar.R 1, Sivabalan.R.V 2 1 Research scholar, Noorul Islam University, Nagercoil, India Assistant Professor, Adhiparasakthi College
More informationAUTOMATION OF ENERGY DEMAND FORECASTING. Sanzad Siddique, B.S.
AUTOMATION OF ENERGY DEMAND FORECASTING by Sanzad Siddique, B.S. A Thesis submitted to the Faculty of the Graduate School, Marquette University, in Partial Fulfillment of the Requirements for the Degree
More informationPERFORMANCE ANALYSIS OF HYBRID FORECASTING MODEL IN STOCK MARKET FORECASTING
PERFORMANCE ANALYSIS OF HYBRID FORECASTING MODEL IN STOCK MARKET FORECASTING Mahesh S. Khadka*, K. M. George, N. Park and J. B. Kim a Department of Computer Science, Oklahoma State University, Stillwater,
More informationApplication of BP Neural Network Model based on Particle Swarm Optimization in Enterprise Network Information Security
, pp.173182 http://dx.doi.org/10.14257/ijsia.2016.10.3.16 Application of BP Neural Network Model based on Particle Swarm Optimization in Enterprise Network Information Security Shumei liu Hengshui University
More informationNetwork Traffic Prediction Based on the Wavelet Analysis and Hopfield Neural Network
Netork Traffic Prediction Based on the Wavelet Analysis and Hopfield Neural Netork Sun Guang Abstract Build a mathematical model is the key problem of netork traffic prediction. Traditional single netork
More informationSanta Fe Artificial Stock Market Model
AgentBased Computational Economics Overview of the Santa Fe Artificial Stock Market Model Leigh Tesfatsion Professor of Economics and Mathematics Department of Economics Iowa State University Ames, IA
More information