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A Rationale for Past/Prediction Span Proportion in Markov Chain-Based Predictive Modeling of Energy-Related Compositional Time Series Data

  • Research Article-Mechanical Engineering
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Abstract

This article aims to investigate the relationship between the available past and desired prediction spans of the compositional time series (CTS) data, while comparatively analyzing the performances of a classical and an alternative approach both based on Markov chain model, in the absence of clear literary guidelines. Contribution lies within the way of selection of optimal CTS proportion span of past and prediction data (past %/prediction %) rather than making a random choice. The various configurations of the experimental CTS data sets of energy proportions have been predicted using the two approaches followed by their mean absolute percentage error analyses within a common range of span proportions from 50–50% to 67–33% in a machine learning fashion. Prediction error has generally a decreasing tendency with increasing past data proportion; however, minimum error can lie anywhere within the tested range of span proportions. So, there is no ready-made choice of span proportion for the purpose of CTS prediction since there exists no clear empirical relationship between span proportion and prediction error. However, the current analysis suggests 59–41% and 58–42% span proportions along CTS as quick choices for comparable compositional prediction using classical and alternative approaches. The former approach is a better choice for CTS data prediction in spite of its inherent shortcoming. The rational may also be applied for the purpose of predictive modeling of other CTS data related to the applications, beyond energy sector, like agriculture, irrigation, marketing, supply chain, transportation, human resources and budget allocation, etc.

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Acknowledgements

The authors would like to pay thanks to the authors of the published literature from which data sets have been referred and employed in this research study.

Funding

This research work is at the cost of the authors’ own income without obtaining any grant from the funding agencies within the public sector, private sector and not-for-profit organizations, etc.

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Engineering and Technology, G.T. Road, Lahore, 54890, Pakistan

    Hussaan Ahmad & Nasir Hayat

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  1. Hussaan Ahmad
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  2. Nasir Hayat
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Correspondence to Hussaan Ahmad.

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Ahmad, H., Hayat, N. A Rationale for Past/Prediction Span Proportion in Markov Chain-Based Predictive Modeling of Energy-Related Compositional Time Series Data. Arab J Sci Eng 47, 15887–15898 (2022). https://doi.org/10.1007/s13369-022-06793-7

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  • DOI: https://doi.org/10.1007/s13369-022-06793-7

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