Revista de Matemática: Teoría y Aplicaciones ISSN Impreso: 1409-2433 ISSN electrónico: 2215-3373

OAI: https://revistas.ucr.ac.cr/index.php/matematica/oai
Comparison of Gap Interpolation Methodologies for water Level time Series using perl/PDL
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Keywords

Interpolation
Regression
Time Series
Interpolación
Regresión
Series de Tiempo

How to Cite

Mostella, A., Sadovski, A., Duff, S., Michaud, P., Tissot, P., & Steidley, C. (2005). Comparison of Gap Interpolation Methodologies for water Level time Series using perl/PDL. Revista De Matemática: Teoría Y Aplicaciones, 12(1-2), 157–164. https://doi.org/10.15517/rmta.v12i1-2.260

Abstract

Extensive time series of measurements are often essential to evaluate long term changes and averages such as tidal datums and sea level rises. As such, gaps in time series data restrict the type and extent of modeling and research which may be accomplished. The Texas A&M University Corpus Christi Division of Nearshore Research (TAMUCC-DNR) has developed and compared various methods based on forward and backward linear regression to interpolate gaps in time series of water level data.

We have developed a software system that retrieves actual and harmonic water level data based upon user provided parameters. The actual water level data is searched for missing data points and the location of these gaps are recorded. Forward and backward linear regression are applied in relation to the location of missing data or gaps in the remaining data. After this process is complete, one of three combinations of the forward and backward regression is used to fit the results. Finally, the harmonic component is added back into the newly supplemented time series and the results are graphed. The software created to implement this process of linear regression is written in Perl along with a Perl module called PDL (Perl Data Language).

Generally, this process has demonstrated excellent results in filling gaps in our water level time series. The program was tested on existing data under three typesof typical weather conditions: calm summers, frontal passages and extreme weather conditions, such as hurricanes. The parameters varied in order to test the accuracy of the methodology included the number of coefficients utilized in the linear regression processes as well as the size of the gaps to be filled. Results are presented for the different weather conditions and the different gap size and coefficient combinations.

https://doi.org/10.15517/rmta.v12i1-2.260
PDF (Español (España))

References

High Performance Computing Development Center, Texas A&M University-Corpus Christi. http://www.sci.tamucc.edu/˜hpcdc/

Mostella, A.; Duff, J.S.; Michaud, P.R. (2002) “Harmpred and Harman: Web-Based Software to Generate Tidal Constituents and Tidal Forecasts for the Texas Coast”, in: Proceedings of the 19th American Meteorological Society Conference on Weather Analysis and Forecasting/15th American Meteorological Society Conference on Numerical Weather Prediction, 12-16 August 2002, San Antonio, Texas.

NOAA (1994) “NOAA Technical Memorandum NOS OES 8”, National Oceanic and Atmospheric Administration, Silver Spring, Marilyn.

Sadovski, A.L.; Michaud, P.R.; Steidley, C.; Tishmack, J.; Torres, K.; Mostella, A.L. (2003) “Integration of statistics and harmonic analysis to predict water levels in estuaries and shallow waters of the Gulf of Mexico”, Presentation at the MATA International Conference (Cancun, Mexico), April 2003.

Sadovski, A.L.; Tissot, P.; Michaud, P.; Steidley, C. (2004) “Statistical and neural network modeling and predictions of tides in the shallow waters of the Gulf of Mexico”, in: WSEAS Transactions on Systems 2(2), WSEAS Press: 301–303.

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