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Cairo University Institute of Statistical Studies and Research Department of Computer and Information Sciences A Novel Forecasting Fuzzy Time Series Model.

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Presentation on theme: "Cairo University Institute of Statistical Studies and Research Department of Computer and Information Sciences A Novel Forecasting Fuzzy Time Series Model."— Presentation transcript:

1 Cairo University Institute of Statistical Studies and Research Department of Computer and Information Sciences A Novel Forecasting Fuzzy Time Series Model Ashraf K. Abd Elaal a, Hesham A. Hefny b, Ashraf H. Abd Elwahab c a Department of Computer and Information Sciences, The High Institute of Computer Science, Al-Kawser city at Sohag, Egypt. b Department of Computer and Information Sciences, Institute of Statistical Studies and Research, Cairo University, Egypt. c Department of Computer and Information Sciences, Electronics Research Institute, National Center for Research, Egypt. November 2009

2 Outlines 1- Introduction 2- Fuzzy Time Series Vs Traditional Time Series forecasting 3- Fuzzy Approaches of Forecasting 4- Proposed Model 5- Comparisons The Forecasting Results for Different Models 6- Conclusion 7- References

3 1- Introduction Forecasting play an important role in our daily life. It is impossible to make a one hundred percent forecast, but researchers can do their best to increase the accuracy of forecasts. Traditional forecasting methods can deal with many forecasting cases, but they cannot solve forecasting problems in which the historical data are linguistic values. Fuzzy time series can solve forecasting problems in which the historical data are linguistic values.

4 2- Fuzzy Time Series Vs Traditional Time Series forecasting The traditional forecasting methods fail to forecast the data with linguistic facts. Time series analysis often requires to turn a non-stationary series into a stationary series. The traditional time series requires more historical data along with some assumptions like normality postulates. The fuzzy forecasting methods success to forecast the data with linguistic facts. Fuzzy time series do not need to turn a non-stationary series into a stationary series and do not require more historical data along with some assumptions like normality postulates

5 3- Fuzzy Approaches of Forecasting [Song and Chissom, 1993] presented the concept of fuzzy time series based on the historical enrollments of the University of Alabama. Fuzzy time series used to handle forecasting problems. Fuzzy time series deal with data without any assumptions like normality, and not requires data normalization, training set and the test set. It works with scant historical data

6 [Song and Chissom, 1993] [Song and Chissom, 1993] presented the time-invariant fuzzy time series model and the time-variant fuzzy time series model based on the fuzzy set theory for forecasting the enrollments of the University of Alabama. They presented a methodology for establish fuzzy time series, most of authors in fuzzy time series field took the same path, but they differ in some steps.

7 [Song and Chissom, 1993] The same steps of the methodology are:- 1- Define the universe of discourse U. U=[Dmin – D1, Dmax + D2] where, Dmin is the minimum value, Dmax is the maximum value, D1, D2 is the positive real numbers to divide the U into n equal length intervals. 2- Partition universal of discourse U into equal intervals.

8 [Song and Chissom, 1993] 4- Fuzzify the historical data. 5- Build fuzzy logic relationships. 3- Define the linguistic terms:-

9 [Song and Chissom, 1993]

10 4- Proposed Model 1- cluster data into c clusters. 2- Determine membership values for each cluster. 3- Rank each cluster. 4- Define the Universe of Discourse U. U=[Dmin – D1, Dmax + D2] Where, Dmin is the minimum value, Dmax is the maximum value, D1, D2 is the positive real numbers to divide the U into n equal length intervals. 5- Partition universal of discourse U into equal intervals. 6- Fuzzify the historical data. 7- Build fuzzy logic relationships. 8- Calculate forecasted outputs:- if X i belong to Y s forecast(t)= Y s else if X j belong Y i,Y k,.. Forecast(t)=midpoint( these intervals) Where X: actual value, Y: cluster value.

11 4- Proposed Model

12 5- Comparisons The Forecasting Results for Different Models Year Actual enrollments Jilani 2008Tsaur 2005Yu 2005Chen 2008Cheng 2008Proposed NRMSE

13 5- Comparisons The Forecasting Results for Different Models

14 6- Conclusion A novel fuzzy time series method based on fuzzy clustering has been proposed. The method of FCMI is integrated in the processes of fuzzy time series to partition datasets. Experimental results on enrollments at the University of Alabama and the comparison with other models: [Jilani and Burney, 2008], [Tsaur, Yang et al. 2005], [Yu-2, 2005], [Chen, Cheng et al. 2008], [Cheng, Wang et al. 2008] show from results that the proposed model is a good model for forecasting values.

15 7- References 1- [Chen, T.-L., C.-H. Cheng, et al., 2008] Chen, T.-L., C.-H. Cheng, et al., "High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets", PhysicaA –888, [Cheng, C.-H., J.-W. Wang, et al., 2008] Cheng, C.-H., J.-W. Wang, et al., "Multi-attribute fuzzy time series method based on fuzzy clustering", Expert Systems with Applications. 34: 1235–1242, [Friedman, M. and A. Kandel, 1999] Friedman, M. and A. Kandel, "Introduction to pattern recognition statistical, structural, neural and fuzzy logic approaches", London, Imperial college press, [Huarng, K., 2001] Huarng, K., "Effective lengths of intervals to improve forecasting in fuzzy time series", Fuzzy Sets and Systems –394, [Jilani, T. A. and S. M. A. Burney, 2008] Jilani, T. A. and S. M. A. Burney, "Multivariate stochastic fuzzy forecasting models", Expert Systems with Applications. 35: 691–700, [Kirchgässner, G. and JürgenWolters, 2007] Kirchgässner, G. and JürgenWolters, "Introduction to modern time series analysis", Berlin, Germany, Springer-Verlag, [Liu, H.-T., 2007] Liu, H.-T., "An improved fuzzy time series forecasting method using trapezoidal fuzzy numbers", Fuzzy Optimization and Decision Making 6(1): 63-80, [Palit, A. K. and D. Popovic, 2005] Palit, A. K. and D. Popovic, "Computational intelligence in time series forecasting theory and engineering applications", London, UK, Springer- Verlag, [Song, Q. and B. S. Chissom-1, 1993] Song, Q. and B. S. Chissom, "Forecasting enrollments with fuzzy time series. I", Fuzzy sets and systems 54(1): 1-9, [Song, Q. and B. S. Chissom-2, 1993] Song, Q. and B. S. Chissom, "New models for forecasting enrollments: fuzzy time series and neural network approaches", eric.ed.gov, 27 DOI: [Tsaur, R.-C., J.-C. Yang, et al., 2005] Tsaur, R.-C., J.-C. Yang, et al., "Fuzzy relation analysis in fuzzy time series model", Computers and Mathematics with Applications , [Yu, H.-K., 2005] Yu, H.-K., "Weighted fuzzy time series models for TAIEX forecasting", Physica A 349: 609–624, 2005.

16 Thank you


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