Non-linear analysis of geomagnetic time series from Etna volcano

Currenti, Gilda; Negro, Ciro Del; Fortuna, Luigi; Napoli, Rosalba; Vicari, Annamaria

doi:10.5194/npg-11-119-2004

Cited by 5 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currenti et al (2004) used local and global linear methods to understand and to describe the dynamics of volcano magnetic signals of Etna Volcano. Laio et al (2004) studied on some nature data and tried to infer about the dynamics of the rainfall-runoff transformation considering river flow time series.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Istanbul monthly temperature by multivariate partial least square

Ertaç

Firuzan

Solum

2014

Theor Appl Climatol

View full text Add to dashboard Cite

Weather forecasting, especially for temperature, has always been a popular subject since it affects our daily life and always includes uncertainty as statistics does. The goals of this study are (a) to forecast monthly mean temperature by benefitting meteorological variables like temperature, humidity and rainfall; and (b) to improve the forecast ability by evaluating the forecasting errors depending upon the parameter changes and local or global forecasting methods. Approximately 100 years of meteorological data from 54 automatic meteorology observation stations of Istanbul that is the mega city of Turkey are analyzed to infer about the meteorological behaviour of the city. A new partial least square (PLS) forecasting technique based on chaotic analysis is also developed by using nonlinear time series and variable selection methods. The proposed model is also compared with artificial neural networks (ANNs), which model nonlinearly the relation between inputs and outputs by working neurons like human brain. Ordinary least square (OLS), PLS and ANN methods are used for nonlinear time series forecasting in this study. Major findings are the chaotic nature of the meteorological data of Istanbul and the best performance values of the proposed PLS model.

show abstract

Section: Introductionmentioning

confidence: 99%

Forecasting Istanbul monthly temperature by multivariate partial least square

Ertaç

Firuzan

Solum

2014

Theor Appl Climatol

View full text Add to dashboard Cite

show abstract

“…However, often global autoregressive models seem more adept at predicting natural geophysical timeseries such as sea surface temperature signals (Hsieh et al, 2005), geomagnetic timeseries associated with volcanic activity (Currenti et al, 2004) and eruptive activity of a volcano (Marzocchi et al, 1997) and electrical precursory timeseries used for earthquake prediction (Cuomo et al, 1998) possibly because of the levels of dynamical noise associated with these signals. Such failures have lead to conclusions that "evidence of chaos in geophysical timeseries does not seem statistically significant" (p. 3207 in Marzocchi et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Detecting nonlinearity in run-up on a natural beach

Bryan

Coco

2007

Nonlin. Processes Geophys.

View full text Add to dashboard Cite

Abstract. Natural geophysical timeseries bear the signature of a number of complex, possibly inseparable, and generally unknown combination of linear, stable non-linear and chaotic processes. Quantifying the relative contribution of, in particular, the non-linear components will allow improved modelling and prediction of natural systems, or at least define some limitations on predictability. However, difficulties arise; for example, in cases where the series are naturally cyclic (e.g. water waves), it is most unclear how this cyclic behaviour impacts on the techniques commonly used to detect the nonlinear behaviour in other fields. Here a non-linear autoregressive forecasting technique which has had success in demonstrating nonlinearity in non-cyclical geophysical timeseries, is applied to a timeseries generated by videoing the waterline on a natural beach (run-up), which has some irregular oscillatory behaviour that is in part induced by the incoming wave field. In such cases, the deterministic shape of each run-up cycle has a strong influence on forecasting results, causing questionable results at small (within a cycle) prediction distances. However, the technique can clearly differentiate between random surrogate series and natural timeseries at larger prediction distances (greater than one cycle). Therefore it was possible to clearly identify nonlinearity in the relationship between observed run-up cycles in that a local autoregressive model was more adept at predicting run-up cycles than a global one. Results suggest that despite forcing from waves impacting on the beach, each run-up cycle evolves somewhat independently, depending on a non-linear interaction with previous run-up cycles. More generally, a key outcome of the study is that oscillatory data provide a similar challenge to differentiating chaotic signals from correlated noise in that the deterministic shape causes an additional source of autocorrelation which in turn influences the predictability at small forecasting distances.

show abstract

Bifurcation and Lyapunov Exponent in Orthogonal Frequency Division Multiplexing

Suansook

Paithoonwattanakij

2008

2008 IEEE International Conference on Computational Cybernetics

View full text Add to dashboard Cite

Non-linear analysis of geomagnetic time series from Etna volcano

Cited by 5 publications

References 22 publications

Forecasting Istanbul monthly temperature by multivariate partial least square

Forecasting Istanbul monthly temperature by multivariate partial least square

Detecting nonlinearity in run-up on a natural beach

Bifurcation and Lyapunov Exponent in Orthogonal Frequency Division Multiplexing

Contact Info

Product

Resources

About