Application of recurrence plots for time series analysis to account for climate changes

Speaker: 

Piotr Sionkowski, BiOSS group

Date: 

03/04/2023 - 11:00

Time series analysis is an important field of data engineering. In this seminar we present the statistical method dedicated to time series analysis, provided the series mimic some form of the complex physical system. The method uses recurrence plot and Hurst exponent approaches. So far, the method was used for data related to molecular dynamics and climate (temperature) observations. Hence, our method tackles very important climate changes issue.  Our method is derived from the existing ones, while the new approach concerns eliminating the human factor in determining parameters of the analysis. Python programming language was used for implementation of the method, and the software will be made available in line with open access policy.

Piotr Sionkowski is a graduate of computer science at the Warsaw University of Technology. He currently works as the chief application security engineer. He is also a member of the interdisciplinary group Biopolymers, Software, Simulations, Statistics (BiOSS). As part of his research on the preparation of a doctorate under the supervision of dr hab. Krzysztof Domino.

More about the research of the BiOSS group.

Author: Jarosław Miszczak | Post date: 27.03.2023 | Last modified: 27.03.2023

Historia zmian

Data aktualizacji: 27/03/2023 - 11:32; autor zmian: Jarosław Miszczak (miszczak@iitis.pl)

Time series analysis is an important field of data engineering. In this seminar we present the statistical method dedicated to time series analysis, provided the series mimic some form of the complex physical system. The method uses recurrence plot and Hurst exponent approaches. So far, the method was used for data related to molecular dynamics and climate (temperature) observations. Hence, our method tackles very important climate changes issue.  Our method is derived from the existing ones, while the new approach concerns eliminating the human factor in determining parameters of the analysis. Python programming language was used for implementation of the method, and the software will be made available in line with open access policy.

Piotr Sionkowski is a graduate of computer science at the Warsaw University of Technology. He currently works as the chief application security engineer. He is also a member of the interdisciplinary group Biopolymers, Software, Simulations, Statistics (BiOSS). As part of his research on the preparation of a doctorate under the supervision of dr hab. Krzysztof Domino.

More about the research of the BiOSS group.
Data aktualizacji: 27/03/2023 - 11:32; autor zmian: Jarosław Miszczak (miszczak@iitis.pl)

Time series analysis is an important field of data engineering. In this seminar we present the statistical method dedicated to time series analysis, provided the series mimic some form of the complex physical system. The method uses recurrence plot and Hurst exponent approaches. So far, the method was used for data related to molecular dynamics and climate (temperature) observations. Hence, our method tackles very important climate changes issue.  Our method is derived from the existing ones, while the new approach concerns eliminating the human factor in determining parameters of the analysis. Python programming language was used for implementation of the method, and the software will be made available in line with open access policy.

Piotr Sionkowski is a graduate of computer science at the Warsaw University of Technology. He currently works as the chief application security engineer. He is also a member of the interdisciplinary group Biopolymers, Software, Simulations, Statistics (BiOSS). As part of his research on the preparation of a doctorate under the supervision of dr hab. Krzysztof Domnio.

More about the research of the BiOSS group.
Data aktualizacji: 27/03/2023 - 10:03; autor zmian: Jarosław Miszczak (miszczak@iitis.pl)

Time series analysis is an important field of data engineering. In this seminar we present the statistical method dedicated to time series analysis, provided the series mimic some form of the complex physical system. The method uses recurrence plot and Hurst exponent approaches. So far, the method was used for data related to molecular dynamics and climate (temperature) observations. Hence, our method tackles very important climate changes issue.  Our method is derived from the existing ones, while the new approach concerns eliminating the human factor in determining parameters of the analysis. Python programming language was used for implementation of the method, and the software will be made available in line with open access policy.

Piotr Sionkowski is a graduate of computer science at the Warsaw University of Technology. He currently works as the chief application security engineer. He is also a member of the interdisciplinary group Biopolymers, Software, Simulations, Statistics (BiOSS). As part of his research on the preparation of a doctorate under the supervision of dr hab. Krzysztof Domnio.

More about the research of the BiOSS group.
Data aktualizacji: 27/03/2023 - 09:51; autor zmian: Jarosław Miszczak (miszczak@iitis.pl)