Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Application of time-universal codes to time series forecasting. / Chirikhin, Konstantin.
Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020. ред. / Alexandre Nketsa; Claude Baron; Clement Foucher. EUROSIS, 2020. стр. 60-63 (Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Application of time-universal codes to time series forecasting
AU - Chirikhin, Konstantin
N1 - Publisher Copyright: © 2020 EUROSIS-ETI. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - As shown in previous research, data compression techniques can be successfully used in time series forecasting. The problem is that there exist many different data compression algorithms and it's unknown in advance which one will be the best for predicting a specific time series. In this study, we use an approach known as time-universal data compression to quickly select a close to optimal algorithm. Its basic idea is to compress only a part of the input data using each of the available compressors in order to select the best one. Then the data is compressed using the selected algorithm only. We implemented this approach and used it to predict real-world data such as sunspot numbers and the ionospheric T-index. The results of our computations show that the approach is quite effective and can be useful in practice.
AB - As shown in previous research, data compression techniques can be successfully used in time series forecasting. The problem is that there exist many different data compression algorithms and it's unknown in advance which one will be the best for predicting a specific time series. In this study, we use an approach known as time-universal data compression to quickly select a close to optimal algorithm. Its basic idea is to compress only a part of the input data using each of the available compressors in order to select the best one. Then the data is compressed using the selected algorithm only. We implemented this approach and used it to predict real-world data such as sunspot numbers and the ionospheric T-index. The results of our computations show that the approach is quite effective and can be useful in practice.
KW - Data compression
KW - Time series forecasting
KW - Universal coding
UR - http://www.scopus.com/inward/record.url?scp=85096763584&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85096763584
T3 - Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020
SP - 60
EP - 63
BT - Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020
A2 - Nketsa, Alexandre
A2 - Baron, Claude
A2 - Foucher, Clement
PB - EUROSIS
T2 - 34th Annual European Simulation and Modelling Conference, ESM 2020
Y2 - 21 October 2020 through 23 October 2020
ER -
ID: 27735044