Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Compression-based methods of time series forecasting. / Chirikhin, Konstantin; Ryabko, Boris.
в: Mathematics, Том 9, № 3, 284, 01.02.2021, стр. 1-11.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Compression-based methods of time series forecasting
AU - Chirikhin, Konstantin
AU - Ryabko, Boris
N1 - Funding Information: Funding: This research was funded by RFBR, project numbers 19-37-90009, 19-47-540001. Publisher Copyright: © 2021 by the authors. Li-censee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/2/1
Y1 - 2021/2/1
N2 - Time series forecasting is an important research topic with many practical applications. As shown earlier, the problems of lossless data compression and prediction are very similar mathemati-cally. In this article, we propose several forecasting methods based on real-world data compressors. We consider predicting univariate and multivariate data, describe how multiple data compressors can be combined into one forecasting method with automatic selection of the best algorithm for the input data. The developed forecasting techniques are not inferior to the known ones. We also propose a way to reduce the computation time of the combined method by using the so-called time-universal codes. To test the proposed techniques, we make predictions for real-world data such as sunspot numbers and some social indicators of Novosibirsk region, Russia. The results of our computations show that the described methods find non-trivial regularities in data, and time universal codes can reduce the computation time without losing accuracy.
AB - Time series forecasting is an important research topic with many practical applications. As shown earlier, the problems of lossless data compression and prediction are very similar mathemati-cally. In this article, we propose several forecasting methods based on real-world data compressors. We consider predicting univariate and multivariate data, describe how multiple data compressors can be combined into one forecasting method with automatic selection of the best algorithm for the input data. The developed forecasting techniques are not inferior to the known ones. We also propose a way to reduce the computation time of the combined method by using the so-called time-universal codes. To test the proposed techniques, we make predictions for real-world data such as sunspot numbers and some social indicators of Novosibirsk region, Russia. The results of our computations show that the described methods find non-trivial regularities in data, and time universal codes can reduce the computation time without losing accuracy.
KW - Artificial intelligence
KW - Data compression
KW - Time series forecasting
KW - Universal coding
UR - http://www.scopus.com/inward/record.url?scp=85100494878&partnerID=8YFLogxK
U2 - 10.3390/math9030284
DO - 10.3390/math9030284
M3 - Article
AN - SCOPUS:85100494878
VL - 9
SP - 1
EP - 11
JO - Mathematics
JF - Mathematics
SN - 2227-7390
IS - 3
M1 - 284
ER -
ID: 27734943