The Markov-exponential grey model for efficient forecasting management

Abstract

This paper presents a mechanism that integrates a grey model (GM) with an exponentially weighted moving average adjustor, known as the EGM. This novel EGM(1,1) model is combined with Markov processes to give the MEGM(1,1) model, which is compared with the original GM(1,1) by exploring the precision of their forecasts. The experimental results show that the average precision of MEGM(1,1) using theoretical models improves from 21.00% to 64.72%, whereas for the empirical models it improves from 40.65% to 96.93%, effectively reducing the error level of the GM(1,1) model.

Keywords

Forecasting model genetic algorithms grey theory Markov chain

References

Kazemi

, Modarres

, Mehregan

M.R.

, Neshat

and Foroughi

, A Markov chain grey forecasting model: A case study of energy demand of industry sector in Iran, The 3rd International Conference on Information and Financial Engineering, Singapore, 2011.

Chang

B.R.

and Tsai

H.F.

, Forecast approach using neural network adaptation to support vector regression grey model and generalized auto-regressive conditional heteroscedasticity, Expert Systems with Applications 34 (2008), 925-934.

Lee

, Lin

C.T.

and Chen

L.H.

, Accuracy analysis of the grey Markov forecasting model, Journal of Statistics & Management Systems 7 (2004), 567-580.

Lewis

, Industrial and Business Forecasting Methods, Butterworth Scientific, London, 1982.

Hsu

C.C.

and Chen

C.Y.

, Applications of improved grey prediction model for power demand forecasting, Energy Conversion and Management 44 (2003), 2241-2249.

Tsai

C.F.

, An intelligent adaptive system for the optimal variable selections of R&D and quality supply chains, Expert Systems with Applications 31 (2006), 808-825.

Tsai

C.F.

, Chao

K.M.

and James

, A dynamic evolutionary mechanism for mixed-production, International Journal of Production Research 46 (2008), 2499-2517.

Tsai

C.F.

and Chao

K.M.

, Chromosome refinement for optimising multiple supply chains, Information Sciences 179 (2009), 2403-2415.

Tsai

C.F.

, Lu

S.L.

, Chen

J.H.

, Chao

K.M.

and Shah

, An adaptable optimizer for green component design, Information Systems and E-Business Management 13 (2015), 193-210.

10.

D.C.

, Yeh

C.W.

and Chang

C.J.

, An improved grey-based approach for early manufacturing data forecasting, Computers & Industrial Engineering 57 (2009), 1161-1167.

11.

Kayacan

, Ulutas

and Kaynak

, Grey system theory-based models in time s series prediction, Expert Systems with Applications 37 (2010), 1784-1789.

12.

Mostafaei

and Kordnoori

, Hybrid grey forecasting model for Iran's energy consumption and supply, International Journal of Energy Economics and Policy 2 (2012), 97-102.

13.

Yidan

H.Y.B.

, Grey-Markov prediction model and its application, Systems Engineering Theory and Practice 12 (1992), 59-63.

14.

Holland

J.H.

, Adaptation in Natural and Artificial Systems, Ann Arbor: The University of Michigan Press, 1975.

15.

Deng

J.L.

, Grey System Fundamental Method, Huazhong University of Science and Technology, Wuhan, China, 1982.

16.

Huang

K.Y.

and Jane

C.J.

, A hybrid model for stock market forecasting and portfolio selection based on ARX, grey system and RS theories, Expert Systems with Applications 36 (2009), 5387-5392.

17.

Juan

and Liu

, Population forecasting in china based on the grey Markov model, International Conference on Information Management, Innovation Management and Industrial Engineering, Shenzhen, China (2011).

18.

Shuang

and Lixia

, The application of grey Markov combined model for port-cargo throughput forecasting, 9th International Conference on Fuzzy Systems and Knowledge Discovery, Chongqing, China (2012).

19.

Shen

R.L.

, Chung

Y.F.

and Chen

T.S.

, A novel application and grey system theory to information security (part I), Computer Standards & Interfaces 31 (2009), 277-281.

20.

Dong

, Chi

, Zhang

and Zhang

, The application of a grey Markov model to forecasting annual maximum water levels at the hydrological stations, Oceanic and Coastal Sea Research 11 (2012), 13-17.

21.

Grimshaw

S.D.

and Alexander

W.P.

, Markov chain models for delinquency: Transition matrix estimation and forecasting, Applied Stochastic Models in Business and Industry 27 (2011), 267-279.

22.

Roberts

S.W.

, Control chart tests based on geometric moving averages, Technometrics 1 (1959), 239-250.

23.

Pai

T.Y.

, Hanaki

, Ho

H.H.

and Hsieh

C.M.

, Using grey system theory to evaluate transportation effects on air quality trends in Japan, Transportation Research Part D-transport and Environment 12 (2007), 158-166.

24.

Kumar

and Jain

V.K.

, Time series models (grey-Markov, grey model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India, Energy 35 (2010), 1709-1716.

25.

Wang

X.P.

and Meng

, Forecasting electricity demand using grey-Markov model, International Conference on Machine Learning and Cybernetics, China (2008).

26.

and Huang

, A grey Markov forecasting model for the electric power requirement in China, 4^th Mexican International Conference on Artificial intelligence (MICAI), Monterrey, Mexico (2005).

27.

Hsu

Y.T.

, Liu

M.C.

, Yeh

and Hung

H.F.

, Forecasting the turning time of stock market based on Markov-Fourier grey model, Expert Systems with Applications 36 (2009), 8597-8603.

28.

Wei

and Jinfu

, Passenger traffic forecast based on the grey Markov method, Proceedings of 2009 IEEE, International Conference on Grey Systems and Intelligent Services, China (2009).