Random Process Intelligent Learnable Extrapolator

Leonid Baranov; Anton Anohin; Il&#039;ya Zherebyatin; Yuncyan Chzhan

doi:10.20295/2412-9186-2025-11-02-137-143

UDC 004.89

RANDOM PROCESS INTELLIGENT LEARNABLE EXTRAPOLATOR Random Process Intelligent Learnable Extrapolator

АА Аа

Published in Transport automation research · Volume 11, Issue 2 · Pages 137–143 · Rubric: INTELLIGENT CONTROL SYSTEMS

DOI: https://doi.org/10.20295/2412-9186-2025-11-02-137-143

Received: 11.06.2025 Accepted: 11.06.2025 Published: 11.06.2025

Authors

Leonid Baranov

Anton Anohin

Il'ya Zherebyatin

Yuncyan Chzhan

Leonid Baranov

Academic degree: doctor of technical sciences

Science rang: professor

Russian University of Transport (MIIT) Russian Federation · Moscow

Anton Anohin

Russian University of Transport (MIIT) Student · Department of Management and Protection of Information · Russian Federation · Moscow

Il'ya Zherebyatin

Russian University of Transport (MIIT) Student · Department of Management and Protection of Information · Russian Federation · Moscow

Yuncyan Chzhan

Russian University of Transport (MIIT) Postgraduate Student · Department of Management and Protection of Information · Russian Federation · Moscow

ABSTRACT

This research presents an accuracy analysis of the prediction of random signals using adaptive and non-adaptive extrapolators. Extrapolation is performed using Chebyshev polynomials orthogonal over a set of equally spaced points. Several algorithms for an automatic selection of an extrapolating polynomial in real time, based on estimates of the prediction error, are considered. A comparative study of these algorithms is carried out in order to identify the most effective one. The study includes an analysis of prediction errors when using fixed-order extrapolators, as well as adaptive extrapolators using different parameter selection rules. In particular, a selection rule based on the minimum of the error modulus at a given time and a rule based on the minimum of the root mean square error for previous cycles are considered. It is shown that the use of a neural network algorithm based on CatBoost provides the smallest prediction error compared to the alternative approaches considered. This demonstrates the potential of using neural networks to adapt extrapolator parameters to changing characteristics of the predicted random process.

KEYWORDS

adaptive extrapolator; Chebyshev polynomials; forecast errors; random process; neural networks

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GOST

Baranov L. A., Anohin A. S., Zherebyatin I. A., Chzhan Y. Random Process Intelligent Learnable Extrapolator // Transport automation research. 2025. no. 2. pp. 137-143. DOI: https://doi.org/10.20295/2412-9186-2025-11-02-137-143. (Date of access 23.09.2026).

MLA

Leonid Baranov, Anohin A. S., Zherebyatin I. A., Chzhan Y.. "Random Process Intelligent Learnable Extrapolator." Transport automation research 11.2 (2025): 137-143. DOI: https://doi.org/10.20295/2412-9186-2025-11-02-137-143.

APA

Baranov L. A., Anohin A. S., Zherebyatin I. A., Chzhan Y. (2025). Random Process Intelligent Learnable Extrapolator. Transport automation research, 11(2), 137-143. https://doi.org/10.20295/2412-9186-2025-11-02-137-143

Chicago

Leonid Baranov, Anohin A. S., Zherebyatin I. A., Chzhan Y.. "Random Process Intelligent Learnable Extrapolator." Transport automation research 11, no. 2 (2025): 137-143. https://doi.org/10.20295/2412-9186-2025-11-02-137-143.

WIKI

{{cite journal | author = Baranov L. A., Anohin A. S., Zherebyatin I. A., Chzhan Y. | title = Random Process Intelligent Learnable Extrapolator | journal = Transport automation research | year = 2025 | volume = 11 | issue = 2 | pages = 137-143 | doi = 10.20295/2412-9186-2025-11-02-137-143 }}

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RANDOM PROCESS INTELLIGENT LEARNABLE EXTRAPOLATOR Random Process Intelligent Learnable Extrapolator

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