Abstract
Time series analysis, has wide applications in statistics, economics, physics and engineering such disciplines. This method used for estimate correctly future values of the variables. In this study, is formed a time series with soil radon gas (222Rn) measurements known as a pioneer of an earthquake. Dynamic system modelling was performed with autoregressive (ARIMA) modelling process by used these measurements. ARIMA; time series analysis is modelled of the recoverable over time a random stochastic (probabilistic) process or its errors. ARIMA model is based on Box-Jenkins model. BoxJenkins model is a statistically based method which is used forward-looking forecasting and control of univariate time series. The obtained results, ARIMA model is indicating success in predict subject
References
- Tanner A.B., 1964. Physical and Chemical Control on Distribution of Radium-226 and Radon-222 in Ground Water near Great Salt Lake, Utah, in The Natural Radiation Environment, edited by J: A. S. Adams and W. M. Lowder, University of Chicago Pres, Chicago, pp. 253-276.
- Planinić J., Vuković B., Radolić V., 2004. Radon time variations and deterministic chaos, Journal of Environmental Radioactivity, 75: 35-45.
- McKenzie D. P., 1972. Active tectonics of the Mediterrannean region, Geophysical Journal of the Royal Astronomical Society, 30: 109-185.
- Külahcı F., Şen Z., 2014. On the correction of spatial and statistical uncertainties in systematic measurements of 222Rn for earthquake prediction, Survey in Geophysics, 35: 449-478.
- Hosking J. R.M., 1996. Asymptotic distributions of the sample mean, autocovariance, and autocorrelations of long- memory time series, Journal of Econometrics, 73: 261-284.
- Sevüktekin M., Nargeleçekenler M., 2005. Zaman Serileri Analizi, Ankara, Nobel Yayın Dağıtım, p. 124-62.
- Narayanan P., Basistha A., Sarkar S., Sachdeva K., 2013. Trend Analysis and ARIMA modeling of pre-monsoon rainfall data for western India, Comtes Rendus Geoscience, 345: 22-27.
- Wang Y., Wang J., Zhao G., Dong Y., 2012. Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China, Energy Policy, 48: 284-294.
- Erdoğan E., 2006. Zaman Serilerinde ARIMA Modelleri, Yüksek Lisans Tezi, Muğla Sıtkı Koçman Üniversitesi, Muğla, 142s.
- Fatih KÜLAHCI e-posta: fatihkulahci@firat.edu.tr
- Seçil NİKSARLIOĞLU e-posta: sniksarlioglu@firat.edu.tr
Abstract
Özet: Zaman serileri analizi, istatistik, ekonomi, fizik ve mühendislik gibi bilim dallarında geniş uygulama alanına sahiptir. Zaman serisi analizi, değişkenlerin gelecekteki değerlerinin doğru bir şekilde tahmin edilmesi için kullanılan bir yöntemdir. Bu çalışmada, bir deprem öncüsü olarak bilinen toprak radon gazı (222Rn) ölçümleri ile bir zaman serisi oluşturulmuştur. Bu veriler kullanılarak, otoregresif süreçler (ARIMA) yardımıyla dinamik sistem modellemesi yapılmıştır. ARIMA; zaman serileri analizinde, zaman içerisinde rastgele gerçekleşen bir stokastik (olasılıksal) sürecin veya hatalarının modellenmesidir. ARIMA modeli, temelde Box-Jenkins modeline dayanmaktadır. Box-Jenkins modeli, tek değişkenli zaman serilerinin ileriye dönük tahmin ve kontrolünde kullanılan istatistiksel tabanlı bir yöntemdir. Elde edilen sonuçlar, ARIMA modellerinin tahmin konusundaki başarısını göstermektedir.
Anahtar kelimeler: Zaman Serileri Analizi, Radon Gazı (222Rn), ARIMA
ARIMA Analysis of Soil Radon (222Rn) Gas Anomalies
Abstract: Time series analysis, has wide applications in statistics, economics, physics and engineering such disciplines. This method used for estimate correctly future values of the variables. In this study, is formed a time series with soil radon gas (222Rn) measurements known as a pioneer of an earthquake. Dynamic system modelling was performed with autoregressive (ARIMA) modelling process by used these measurements. ARIMA; time series analysis is modelled of the recoverable over time a random stochastic (probabilistic) process or its errors. ARIMA model is based on Box-Jenkins model. Box-Jenkins model is a statistically based method which is used forward-looking forecasting and control of univariate time series. The obtained results, ARIMA model is indicating success in predict subject.
Key words: Time Series Analysis, Radon Gas (222Rn), ARIMA
References
- Tanner A.B., 1964. Physical and Chemical Control on Distribution of Radium-226 and Radon-222 in Ground Water near Great Salt Lake, Utah, in The Natural Radiation Environment, edited by J: A. S. Adams and W. M. Lowder, University of Chicago Pres, Chicago, pp. 253-276.
- Planinić J., Vuković B., Radolić V., 2004. Radon time variations and deterministic chaos, Journal of Environmental Radioactivity, 75: 35-45.
- McKenzie D. P., 1972. Active tectonics of the Mediterrannean region, Geophysical Journal of the Royal Astronomical Society, 30: 109-185.
- Külahcı F., Şen Z., 2014. On the correction of spatial and statistical uncertainties in systematic measurements of 222Rn for earthquake prediction, Survey in Geophysics, 35: 449-478.
- Hosking J. R.M., 1996. Asymptotic distributions of the sample mean, autocovariance, and autocorrelations of long- memory time series, Journal of Econometrics, 73: 261-284.
- Sevüktekin M., Nargeleçekenler M., 2005. Zaman Serileri Analizi, Ankara, Nobel Yayın Dağıtım, p. 124-62.
- Narayanan P., Basistha A., Sarkar S., Sachdeva K., 2013. Trend Analysis and ARIMA modeling of pre-monsoon rainfall data for western India, Comtes Rendus Geoscience, 345: 22-27.
- Wang Y., Wang J., Zhao G., Dong Y., 2012. Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China, Energy Policy, 48: 284-294.
- Erdoğan E., 2006. Zaman Serilerinde ARIMA Modelleri, Yüksek Lisans Tezi, Muğla Sıtkı Koçman Üniversitesi, Muğla, 142s.
- Fatih KÜLAHCI e-posta: fatihkulahci@firat.edu.tr
- Seçil NİKSARLIOĞLU e-posta: sniksarlioglu@firat.edu.tr
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Metrology, Applied and Industrial Physics |
| Journal Section | Makaleler |
| Authors | |
| Publication Date | December 31, 2014 |
| Published in Issue | Year 2014 Volume: 9 Issue: 2 |
