Application of time-universal codes to time series forecasting

Standard

Application of time-universal codes to time series forecasting. / Chirikhin, Konstantin.

Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020. ed. / Alexandre Nketsa; Claude Baron; Clement Foucher. EUROSIS, 2020. p. 60-63 (Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Chirikhin, K 2020, Application of time-universal codes to time series forecasting. in A Nketsa, C Baron & C Foucher (eds), Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020. Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020, EUROSIS, pp. 60-63, 34th Annual European Simulation and Modelling Conference, ESM 2020, Toulouse, France, 21.10.2020.

APA

Chirikhin, K. (2020). Application of time-universal codes to time series forecasting. In A. Nketsa, C. Baron, & C. Foucher (Eds.), Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020 (pp. 60-63). (Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020). EUROSIS.

Vancouver

Chirikhin K. Application of time-universal codes to time series forecasting. In Nketsa A, Baron C, Foucher C, editors, Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020. EUROSIS. 2020. p. 60-63. (Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020).

Author

Chirikhin, Konstantin. / Application of time-universal codes to time series forecasting. Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020. editor / Alexandre Nketsa ; Claude Baron ; Clement Foucher. EUROSIS, 2020. pp. 60-63 (Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020).

BibTeX

@inproceedings{60f9d9eb25a145909218f5970e79526b,

title = "Application of time-universal codes to time series forecasting",

abstract = "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.",

keywords = "Data compression, Time series forecasting, Universal coding",

author = "Konstantin Chirikhin",

note = "Publisher Copyright: {\textcopyright} 2020 EUROSIS-ETI. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 34th Annual European Simulation and Modelling Conference, ESM 2020 ; Conference date: 21-10-2020 Through 23-10-2020",

year = "2020",

language = "English",

series = "Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020",

publisher = "EUROSIS",

pages = "60--63",

editor = "Alexandre Nketsa and Claude Baron and Clement Foucher",

booktitle = "Modelling and Simulation 2020 - The European Simulation and Modelling Conference, ESM 2020",

}

RIS

TY - GEN

T1 - Application of time-universal codes to time series forecasting

AU - Chirikhin, Konstantin

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