One-parameter fractional linear prediction

Vladimir Despotovic*, Tomas Skovranek, Zoran Peric

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)


The one-parameter fractional linear prediction (FLP) is presented and the closed-form expressions for the evaluation of FLP coefficients are derived. Contrary to the classical first-order linear prediction (LP) that uses one previous sample and one predictor coefficient, the one-parameter FLP model is derived using the memory of two, three or four samples, while not increasing the number of predictor coefficients. The first-order LP is only a special case of the proposed one-parameter FLP when the order of fractional derivative tends to zero. Based on the numerical experiments using test signals (sine test waves), and real-data signals (speech and electrocardiogram), the hypothesis for estimating the fractional derivative order used in the model is given. The one-parameter FLP outperforms the classical first-order LP in terms of the prediction gain, having comparable performance with the second-order LP, although using one predictor coefficient less.

Original languageEnglish
Pages (from-to)158-170
Number of pages13
JournalComputers and Electrical Engineering
Publication statusPublished - Jul 2018
Externally publishedYes


  • Fractional calculus
  • Fractional derivative
  • Linear prediction
  • Optimal prediction


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