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Nearest Neighbor Forecasting Using Sparse Data Representation

In: Mathematical Analysis in Interdisciplinary Research

Author

Listed:
  • Dimitrios Vlachos

    (University of Peloponnese)

  • Dimitrios Thomakos

    (National and Kapodistrian University of Athens)

Abstract

The method of the nearest neighbors as well as its variants have proven to be very powerful tools in the non-parametric prediction and categorization of experimental measurements. On the other hand, the number of data available today as well as their dimensionality and complexity is growing rapidly in many scientific fields, such as economics, biology, chemistry, medicine, and others. Usually, the data and their characteristics have semantic dependence and a lot of noise. At this point, the sparse data representation that deals with these problems with great success is involved. In this paper we present the application of these two tried and tested techniques for prediction in various fields related to economics. New techniques are presented as well as exhaustive tests for the evaluation of the proposed methods. The results are encouraging to continue research into the possibilities of sparse representation combined with good proven machine learning techniques.

Suggested Citation

  • Dimitrios Vlachos & Dimitrios Thomakos, 2021. "Nearest Neighbor Forecasting Using Sparse Data Representation," Springer Optimization and Its Applications, in: Ioannis N. Parasidis & Efthimios Providas & Themistocles M. Rassias (ed.), Mathematical Analysis in Interdisciplinary Research, pages 1003-1024, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-84721-0_38
    DOI: 10.1007/978-3-030-84721-0_38
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