Our team at the Department of Physics (School of Science, Lamia, Greece) has extended experience in several areas of system dynamics and time series analysis as well as modeling across various time and length scales).

System Dynamics studied through time series analysis using advanced methods such as methods based on phase space reconstruction,

Hurst analysis, mutual information, cross correlation, Recurrence plots, Cross recurrence plots, Granger causality etc.

The analysis using advanced methods has as objective

• System Identification

• Spatiotemporal phenomena

• Dynamical transitions

• Identification of events

• Big data analysis

• Event Detection

• Temporal and spatial correlation analysis

• Causality effects between correlated variables.

• Prediction

• Clustering and similarities

• Fuzzy clustering

• Multiscale time series analysis

Applications include study of experimental, simulation and field data in areas such as

• Turbulence fluid flows

• Environmental time series

• Sensor collected Data

• Field Measurements: boys, radar rain time series, wind velocity

• Traffic data analysis (for accident detection)

• Other engineering problems

• Atomistic simulation data

• Financial data

• Biological molecules

• Motorcyclists behavior

Analysis can be applied also to Industrial process data, financial, environmental data, spatially varying quantities etc, biological signals like EEG, damage detection, physiological signals (heart rate), internet traffic etc, managements of energy grids, building efficiency etc.

Local team members

• Prof. Theodore KARAKASIDIS (Principal investigator)

• Dr. Filippos SOFOS ( Assitant Professor, under appointment)

• Dr. Avraam CHARAKOPOULOS (Research Associate)

• Dr. Evangelos KARVELAS (Research Associate)

PhD Students

• Kostas STERGIOU (From Msc Econophyscis-Financial Predictions)

• Christos LIOSIS

MSc Students

• Kostas NTINOPOULOS (From Msc Econophyics)

• Kostas PAPASTAMATIOU (From Msc Econophyics-Financial Predictions)

Graduate Students

Research collaborations in Greece

Inter-departmental collaborations

Department of Civil Engineering, University of Thessaly, Greece

Department of Civil Engineering, National Technical University of Athens, Greece

Department of Mathematics, University of Patras, Greece

Department of Energy Technology Engineering, Technological Institute Athens, Greece

Innosec, (SME in information security) Thessaloniki, Greece

Research collaborations abroad

University Joseph Fourier, Grenoble

International School of the Hague

University of Santiago de Compostela

Research projects

•1-Nov-05 to 31-Aug-08 Research Project “Numerical modeling and experimental study of flows in micro and nano-channels” funded by the Greek Ministry of Development, Greek Secretariat of Research and Technology.

(Budget 120.000 Euros)

• Dr. Karakasidis was visiting researcher at the University Joseph Fourier, Grenoble France, june 2012

Representative journal papers

1. Charakopoulos, A. Κ., Karakasidis, T. E., & Liakopoulos, A. (2015). Spatiotemporal Analysis of Seawatch Buoy Meteorological Observations. Environmental Processes, 2(1), 23-39.

Abstract

In the present study, we analyzed meteorological observations from Seawatch buoys in the Mediterranean Sea and specifically from locations in the Aegean and Ionian Sea. The data were collected from buoys that have been deployed by the Hellenic Center for Marine Research (HCMR) in the framework of the POSEIDON project. Our aim was to understand the spatiotemporal underlying characteristics of the meteorological conditions and identify correlation patterns between different locations. For each time series at a given buoy location we estimated mainly non-linear measures such as mutual information combined with simple descriptive statistic measures, as well as some other dynamics detectors such as Hurst Exponent and Hjorth parameters. Furthermore, the relationships between the meteorological variables were investigated using the cross correlation method as well as Granger causality methodology for identification of direction interactions among variables. The results show that the combination of the proposed methods reveals information about the spatiotemporal characteristics of the time series.

Fragkou, A. D., Karakasidis, T. E., & Nathanail, E. (2015) Non-linear Time series Methods Applications on Transport Data. International Conference ‘Science in Technology SCinTE 2015, Athens Greece

Abstract

In the present study we present results of the application of nonlinear time series analysis on traffic data. More specifically we analyze records of one day values of Attiki Odos collected from sensors at several measurement nodes. The data are analyzed using the Recurrence Quantification Analysis (RQA) in order to detect traffic incidents. The analysis indicates that RQA can contribute in the detection of traffic incidents by locating abrupt changes of system’s dynamics through RQA measures such as recurrence, determinism, maxline,laminarity,and trapping time.

Lemonakis, P. V., Eliou, N. E., Karakasidis, T., & Botzoris, G. (2014). A new methodology for approaching motorcycle riders’ behavior at curved road sections. European Transport Research Review, 6(3), 303-314.

Objective

The present paper focuses on the investigation of motorcycle riders’ behavior at curved road sections by introducing a new methodology based on global positioning system (GPS) technology. In the frame of the research, the determination of the optimum regression curve between the curve radius’ and the corresponding velocities, was investigated.

Within the context of the paper field measurements were conducted, with the use of appropriate velocity recording equipment in order to confirm the efficiency of the proposed methodology. The measurements were conducted taking into account various factors that potentially influence riders’ behavior such as the different light conditions, the difference on riding experience level, the familiarity of the riders with the routes, the presence of pillion and the different road environments, such as width/condition of the road pavement, roadside land use, right/left hand curves etc.

The experimental environment that served the needs of the experiment was mountain Pelion in Magnesia region in Greece and was based upon four primary conditions: the location, the type of the road, the weather conditions and finally, the time and date that the experiment would be conducted.

The validation of the proposed methodology was performed by recruiting two motorcyclists. Their selection was based on demographic, psychometric and experience criteria.

The research showed among others, that the regression curves could be used as a curve classification mean. Moreover, a significant variation was detected on the riders’ behavior when carrying a pillion related to their experience levels.

Fragkou, A. D., Karakasidis, T. E., Sarris, I. E., & Liakopoulos, A. (2015). Spatiotemporal time series analysis methods for the study of turbulent magnetohydrodynamic channel flows. Environmental Processes, 2(1), 141-158.

Charakopoulos, A. Κ., Karakasidis, T. E., Papanicolaou, P. N., & Liakopoulos, A. (2014). The application of complex network time series analysis in turbulent heated jets. Chaos: An Interdisciplinary Journal of Nonlinear Science, 24(2), 024408.

Abstract

In the present study, we applied the methodology of the complex network-based time series analysis to experimental temperature time series from a vertical turbulent heated jet. More specifically, we approach the hydrodynamic problem of discriminating time series corresponding to various regions relative to the jet axis, i.e., time series corresponding to regions that are close to the jet axis from time series originating at regions with a different dynamical regime based on the constructed network properties. Applying the transformation phase space method (k nearest neighbors) and also the visibility algorithm, we transformed time series into networks and evaluated the topological properties of the networks such as degree distribution, average path length, diameter, modularity, and clustering coefficient. The results show that the complex network approach allows distinguishing, identifying, and exploring in detail various dynamical regions of the jet flow, and associate it to the corresponding physical behavior. In addition, in order to reject the hypothesis that the studied networks originate from a stochastic process, we generated random network and we compared their statistical properties with that originating from the experimental data. As far as the efficiency of the two methods for network construction is concerned, we conclude that both methodologies lead to network properties that present almost the same qualitative behavior and allow us to reveal the underlying system dynamics.

Charakopoulos, A. K., Karakasidis, T. E., Papanicolaou, P. N., & Liakopoulos, A. (2014). Nonlinear time series analysis and clustering for jet axis identification in vertical turbulent heated jets. Physical Review E, 89(3), 032913.

Karakasidis, T. E., Liakopoulos, A., Fragkou, A., & Papanicolaou, P. (2009). Recurrence quantification analysis of temperature fluctuations in a horizontal round heated turbulent jet. International Journal of Bifurcation and Chaos, 19(08), 2487-2498.

Karakasidis, T. E., & Charakopoulos, A. (2009). Detection of low-dimensional chaos in wind time series. Chaos, Solitons & Fractals, 41(4), 1723-1732.

Karakasidis, T. E., Fragkou, A., & Liakopoulos, A. (2007). System dynamics revealed by recurrence quantification analysis: Application to molecular dynamics simulations. Physical Review E, 76(2), 021120.

Georgiou, D. N., Karakasidis, T. E., Nieto, J. J., & Torres, A. (2009). Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. Journal of theoretical biology, 257(1), 17-26.

Samaras, P., Kungolos, A., Karakasidis, T., Georgiou, D., & Perakis, K. (2001). Statistical evaluation of PCDD/F emission data during solid waste combustion by fuzzy clustering techniques. Journal of Environmental Science and Health, Part A, 36(2), 153-161.