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A Business Intelligence Technique for Forecasting the Automobile Sales using Adaptive Intelligent Systems (ANFIS and ANN)
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 74 - Number 9 |
| Year of Publication: 2013 |
| Authors: Alekh Dwivedi, Maheshwari Niranjan, Kalicharan Sahu |
10.5120/12911-9383
|
Alekh Dwivedi, Maheshwari Niranjan, Kalicharan Sahu . A Business Intelligence Technique for Forecasting the Automobile Sales using Adaptive Intelligent Systems (ANFIS and ANN). International Journal of Computer Applications. 74, 9 ( July 2013), 7-13. DOI=10.5120/12911-9383
Abstract
Today, Sales forecasting plays a key role for each business in this competitive environment. The forecasting of sales data in automobile industry has become a primary concern to predict the accuracy in future sales. This work addresses the problem of monthly sales forecasting in automobile industry (maruti car). The data set is based on monthly sales (past 5 year data from 2008 to 2012). Primarily, we used two forecasting methods namely Moving Average and Exponential smoothing to forecast the past data set and then we use these forecasted values as a input for ANFIS (Adaptive Neuro Fuzzy Inference System). Here, MA and ES forecasted values used as input variable for ANFIS to obtain the final accurate sales forecast. Finally we compare our model with two other forecasting models: ANN (Artificial Neural Network) and Linear Regression. Empirical results demonstrate that the ANFIS model gives better results out than other two models.
References
- Chang, P. , C. , Liu, C. H. , and Fan, C. Y. , Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry, Knowledge-Based Systems, Volume 22, Issue 5, Pages 344355,2009.
- Abu-Eisheh, S. A. , & Mannering, F. (2002). Forecasting automobile demand for economies in transition, a dynamic simultaneous-equation system approach. Transportation Planning and Technology, 25, 311–331.
- Kuo, R. J. , Wu, P. , and Wang, C. P. , An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination, Neural Netw. Sep;15(7):909-25. , 2002.
- Fiordaliso, A. (1998). A nonlinear forecasts combination method based on Takagi-Sugeno fuzzy systems. International journal of forecasting, 14:367-379.
- Makridakis, S. and Wheelwright, S. C. , In Forecasting Methods for Management, 5th Ed. , Wiley, Chichester (1989).
- Winklhofer, H. and Diamantopoulos, A. , "A model of export sales forecasting behavior and performance: development and testing," International Journal of Forecasting, Vol. 19, pp. 271-285 (2003).
- Mentzer, J. T. and Bienstock, C. C. , Sales Forecasting Management: Understanding the Techniques, Systems and Management of the Sales Forecasting Process, Sage publications, Thousand Oaks, CA (1998).
- Diamantopoulos, A. and Winklhofer, H. , "Export sales forecasting by UK firms technique utilization and impact on forecast accuracy," Journal of Business Research, Vol. 56, pp. 45-54 (2003).
- C. C. Holt, Forecasting seasonals and trends by exponen-tially weighted moving averages. International Journal of Forecasting 20(1), pp. 5-10, 2004.
- Wood, D. and Dasgupta, B. , "Classifying trend movements in the MSCI U. S. A. capital market index-a comparison of regression, aroma and neural network methods," Computers & Operations Research, Vol. 23,No. 6, pp. 611-622 (1996).
- Shtub, A. , Versano, R. , Estimating the cost of steel pipe bending, a comparison between neural networks and regression analysis, International Journal of Production Economics, vol. 62, no. 3, pp. 201-207, 1999.
- Jang, J. S. R. , & Gulley, N. (1995). The fuzzy logic toolbox for use with MATLAB. Natick, MA:The Math WorksInc. .
- Firat, M. , & Güngör, M. (2008). Hydrological time series modeling using an adaptive neuro-fuzzy inference system. Hydrological Processes, 22, 2122–2132.
- K. Y. Chen, Combining linear and nonlinear model in fore-casting tourism demand, Expert Systems with Applications 38(8), pp. 10368-10376, 2011.
- Martino, J. P. , Technological forecasting for decision mak-ing (3rd ed. ). New York: McGraw-Hill, 1993.
- Boyacioglu, M. A. , Avci, D. , An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange, Expert Systems with Applications: An International Journal , Volume 37 Issue 12, December, 2010.
- S. H. Lim, Forecasting models of additional use of mobile digital contents: A comparison of artificial neural networks and logistic regression analysis, International Journal of Computer Science and Network Security 6(6), 146-149,2006.
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