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A Tabu Search heuristic procedure in Markov chain bootstrapping

Author

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  • Cerqueti, Roy
  • Falbo, Paolo
  • Guastaroba, Gianfranco
  • Pelizzari, Cristian

Abstract

Markov chain theory is proving to be a powerful approach to bootstrap finite states processes, especially where time dependence is non linear. In this work we extend such approach to bootstrap discrete time continuous-valued processes. To this purpose we solve a minimization problem to partition the state space of a continuous-valued process into a finite number of intervals or unions of intervals (i.e. its states) and identify the time lags which provide “memory” to the process. A distance is used as objective function to stimulate the clustering of the states having similar transition probabilities. The problem of the exploding number of alternative partitions in the solution space (which grows with the number of states and the order of the Markov chain) is addressed through a Tabu Search algorithm. The method is applied to bootstrap the series of the German and Spanish electricity prices. The analysis of the results confirms the good consistency properties of the method we propose.

Suggested Citation

  • Cerqueti, Roy & Falbo, Paolo & Guastaroba, Gianfranco & Pelizzari, Cristian, 2013. "A Tabu Search heuristic procedure in Markov chain bootstrapping," European Journal of Operational Research, Elsevier, vol. 227(2), pages 367-384.
    • Handle: RePEc:eee:ejores:v:227:y:2013:i:2:p:367-384
    DOI: 10.1016/j.ejor.2012.11.009
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    References listed on IDEAS

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    Cited by:

    1. Eduard Gabriel Ceptureanu & Sebastian Ceptureanu & Claudiu Herteliu, 2021. "Evidence regarding external financing in manufacturing MSEs using partial least squares regression," Annals of Operations Research, Springer, vol. 299(1), pages 1189-1202, April.
    2. Cerqueti, Roy & Falbo, Paolo & Pelizzari, Cristian, 2017. "Relevant states and memory in Markov chain bootstrapping and simulation," European Journal of Operational Research, Elsevier, vol. 256(1), pages 163-177.
    3. Roy Cerqueti & Paolo Falbo & Cristian Pelizzari & Federica Ricca & Andrea Scozzari, 2017. "A mixed integer linear program to compress transition probability matrices in Markov chain bootstrapping," Annals of Operations Research, Springer, vol. 248(1), pages 163-187, January.

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