# Explanation of fluctuations in water use time series

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## Abstract

This paper presents techniques for studying the influence of the climatic and other variables for the explanation of the water use with an example of time series in Gainesville, Florida. A statistical methodology is described for separating the different time scale components in time series of water use, namely, long term component, seasonal component, and short term component. We analyze each component separately and we prove that the temperature, precipitation, soil temperature, and relative humidity time series are the main climatic factors for the explanation of the long term, seasonal and short term component of the water use time series. Part of the residuals derived from the linear regression of the long term component of the water use can be explained by the unemployment rate. We also show that with the decomposition of the water use time series the explanation of the water use has been improved approximately two times. The explanation of the long term component of water use by the long term regional weather parameters can enable us to the long term regional prediction of the water resources availabilities. This methodology can be applied for studying the water use time series in other locations, as well.

## Keywords

Climatic factors Decomposition of time scales Filtration of time series KZ filter Seasonal component## Preview

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## References

- Close B, Zurbenko I (2011) Kolmogorov–Zurbenko adaptive algorithm. In: JSM proceedings, statistical computing and analysts sectionGoogle Scholar
- Eskridge RE, Ku JU, Porter PS, Rao ST, Zurbenko I (1997) Separating different scales of motion in time series of meteorological variables. Bull Am Meteorol Soc 78: 1473–1483CrossRefGoogle Scholar
- Hogrefe C, Rao ST, Zurbenko IG, Porter PS (2000) Interpreting the information in ozone observations and model predictions relevant to regulatory policies in the Eastern United States. Bull Am Meteorol Soc Bulletin 81: 2083–2106CrossRefGoogle Scholar
- Horbelt S, Munoz Barrutia A, Blu T, Unser M (2000) Spline kernels for continuous-space image processing. In: Proceedings of the twenty fifth IEEE international conference on acoustics, speech and signal processing (ICASSP), Istanbul, Turkey, pp 2191–2194Google Scholar
- Maidment D, Parzen E (1984) Cascade model of monthly municipal water use. Water Resour Res 20: 15–23CrossRefGoogle Scholar
- Maidment D, Miaou SP (1986) Daily water use in nine cities. Water Resour Res 22: 845–851CrossRefGoogle Scholar
- Mays LW, Tung YK (1992) Hydrosystems engineering and management. McGraw-Hill, New YorkGoogle Scholar
- OECD (2005) Data and metadata reporting and presentation handbook, OECD, Paris, section 4: guidelines for the reporting of different forms of dataGoogle Scholar
- Rao ST, Zurbenko IG (1994) Detecting and tracking changes in ozone air quality. J Air Waste Manag Assoc 44: 1089–1092Google Scholar
- Rao ST, Zurbenko IG, Neagu R, Porter PS, Ku JY, Henry RF (1997) Space and time scales in ambient ozone data. Bull Am Meteorol Soc 78: 2153–2166CrossRefGoogle Scholar
- Tsakiri KG, Zurbenko IG (2008) Destructive effect of the noise in principal component analysis with application to ozone pollution. In: JSM proceedings, statistical computing section. American Statistical Association, Alexandria, pp 3054–3068Google Scholar
- Tsakiri KG, Zurbenko IG (2009) Model prediction of ambient ozone concentrations. In: JSM proceedings, statistical computing section. American Statistical Association, Alexandria, pp 3054–3068Google Scholar
- Tsakiri KG, Zurbenko IG (2010a) Prediction of ozone concentrations using atmospheric variables. J Air Qual Atmos Health. http://www.springerlink.com/content/14512x4012271724/
- Tsakiri KG, Zurbenko IG (2010b) Determining the main atmospheric factor on ozone concentrations. J Meteorol Atmos Phys 109:129–137. http://www.springerlink.com/content/h21005561l0n673j/ Google Scholar
- Tsakiri K, Zurbenko IG (2011) Effect of noise in principal component analysis. J Stat Math 2:40–48. ISSN: 0976-8807 & E-ISSN: 0976-8815Google Scholar
- Unser M, Aldroubi A, Eden M (1993) B-spline signal processing: Part I-theory and Part II-efficient design. IEEE Trans Signal Proc 41: 821–848CrossRefGoogle Scholar
- Webb AR (1996) An approach to non-linear principal component analysis using radially symmetric kernel functions. McGraw-Hill, New York pp 159–168Google Scholar
- Wikipedia (2010) Kolmogorov–Zurbenko Filters. http://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Zurbenko_filter
- Yang W, Zurbenko I (2006) kzft: Kolmogorov–Zurbenko Fourier transform and application, R packageGoogle Scholar
- Yang W, Zurbenko I (2010a) Nonstationarity Wiley interdisciplinary revives. In: Proceedings of computational statistics, Wiley 2, pp 107–115Google Scholar
- Yang W, Zurbenko I (2010b) Kolmogorov–Zurbenko filters, Wiley interdisciplinary revives. In: Proceedings of computational statistics, Wiley 2, pp 340–351. http://wires.wiley.com/WileyCDA/WiresArticle/wisId-WICS71.html
- Zurbenko IG (1986) The spectral analysis of time series. North Holland Series in Statistics and Probability, AmsterdamGoogle Scholar
- Zurbenko IG, Sowizral M (1999) Resolution of the destructive effect of noise on linear regression of two time series. Far East J Theor 3: 139–157Google Scholar
- Zurbenko IG, Cyr DD (2011) Climate fluctuations in time and space. Clim Res 46:67–76. http://www.int-res.com/abstracts/cr/v46/n1/p67-76/ Google Scholar