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Developing a Fuzzy Expert System Based on a Hybrid Artificial Intelligence Model for Sales Forecasting Modeling

Hadavandi, Esmaeil | 2009

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 40056 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Shavandi, Hassan
  7. Abstract:
  8. Success in forecasting and analyzing sales for a given good or service can mean the difference between profit and loss for an accounting period and, ultimately, the success or failure of the business itself. Therefore reliable prediction of sales becomes a very important task. This thesis presents a novel sales forecasting approach by integration of Genetic Fuzzy Systems (GFS) and Data Clustering to construct a sales forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on sales. At the next stage we divide our raw data into k clusters by means of K-means algorithm. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. In order to evaluate our K-means Genetic Fuzzy System (KGFS) we apply it on Printed Circuit Board (PCB) sales forecasting problem which have been used as the case in different studies. We compare the performance of extracted expert system with previous sales forecasting methods using mean absolute percentage error (MAPE) and root mean square error (RMSE). Experimental results show that the proposed approach outperforms the other previous approaches. so it can be considered as a suitable tool for sales forecasting problems
  9. Keywords:
  10. Fuzzy-Genetic System ; Stepwise Regression ; Fuzzy Expert System ; Sales Forecasting ; Data Clustering

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