Earthquake Forecasting Based on Multi-State System Methodology

Karagrigoriou, Alex; Makrides, Andreas; Tsapanos, Theodoros M.; Vougiouka, G.

doi:10.1007/s11009-015-9451-x

Cited by 7 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, a beginning of an epidemic trend is a change point whose timely detection will predict occurrence of a new epidemic. Similar explanations as the ones given above could be used in several scientific fields that incidence data occur such as seismology 31–33 and meteorology. 34…”

Section: Changepoint Detection Periodic Arma Modeling and Estimatmentioning

confidence: 90%

Periodic-type auto-regressive moving average modeling with covariates for time-series incidence data via changepoint detection

Kalligeris

Karagrigoriou

Parpoula

2019

Stat Methods Med Res

View full text Add to dashboard Cite

When it comes to incidence data, most of the work on this field focuses on the modeling of nonextreme periods. Several attempts have been made and a variety of techniques are available to achieve so. In this work, in order to model not only the nonextreme periods but also capture the behavior of the whole time-series, we make use of a dataset on influenza-like illness rate for Greece, for the period 2014–2016. The identification of extreme periods is made possible via changepoint detection analysis and model selection techniques are developed in order to identify the optimal periodic-type auto-regressive moving average model with covariates that best describes the pattern of the time-series. In addition, in the context of incidence data modeling, an advanced algorithm was developed in order to improve the accuracy of the selected model. The derived results are satisfactory since the changepoint method seems to identify correctly the extreme periods, and the selected model: (1) estimates accurately the influenza-like illness syndrome morbidity burden in the case of Greece, and (2) captures satisfactorily enough the behavior of the whole time-series.

show abstract

Section: Changepoint Detection Periodic Arma Modeling and Estimatmentioning

confidence: 90%

Periodic-type auto-regressive moving average modeling with covariates for time-series incidence data via changepoint detection

Kalligeris

Karagrigoriou

Parpoula

2019

Stat Methods Med Res

View full text Add to dashboard Cite

show abstract

“…Applications can be found in most scientific fields from finance and business to engineering, biosciences, climatology, geosciences etc. [9][10][11].…”

Section: Step 1-the Change Point Detectionmentioning

confidence: 99%

An Advanced Markov Switching Approach for the Modelling of Consultation Rate Data

Kalligeris

Karagrigoriou

Parpoula

2021

The 7th International Conference on Time Series and Forecasting

View full text Add to dashboard Cite

show abstract

“…Seismic hazard evaluation in the Japan area using Markov chains was studied by Nava et al (2005). Karagrigoriou et al (2015) made an attempt to describe zoning data as data of a multi-state system through Markov model and examine earthquake occurrence by assessing intensity rates and transition probabilities in seismic zones of South America. Spatio-temporal complex Markov chain used by Cavers and Vasudevan (2015) in global earthquake sequences and analyze the statistics of the transition probabilities linked to earthquake zones.…”

Section: Introductionmentioning

confidence: 99%

On the Earthquake Occurrences in Japan and the Surrounding Area via Semi Markov Modeling

2017

Self Cite

View full text Add to dashboard Cite

In the present paper, the earthquake occurrences in the area of Japan, are studied by a semi Markov model which is considered homogeneous in time. The data applied refer to earthquakes of large magnitude (Mw>6.0) during the period 1900-2012. We consider 9 seismic zones derived from the typical 11 zones for the area of Japan, due to the lack of data for 3 zones (9-th,10-th and 11-th). Also, we define 3 groups for the magnitudes, corresponding to 6-7,7.1-8 and M> 8.0. Thus, we consider for our semi Markov model a finite state space, S={ ( ,)j i ZR | i=1,...9, j=1,2,3}, where i Z defines the i-th seismic zone and j R states the j-th magnitude scale. We applied the data to describe the interval transition probabilities for the states and the model's limiting behaviour for which is sufficient an interval of time of seven years. The time unit of the model is considered to be one day. Some interesting results, concerning the interval transition probabilities and the limiting state vector, are derived.

show abstract

Earthquake Forecasting Based on Multi-State System Methodology

Cited by 7 publications

References 23 publications

Periodic-type auto-regressive moving average modeling with covariates for time-series incidence data via changepoint detection

Periodic-type auto-regressive moving average modeling with covariates for time-series incidence data via changepoint detection

An Advanced Markov Switching Approach for the Modelling of Consultation Rate Data

On the Earthquake Occurrences in Japan and the Surrounding Area via Semi Markov Modeling

Contact Info

Product

Resources

About