Time Series Analysis: A Hydrological Prospective


Time Series Analysis: A Hydrological Prospective


Sirisha Adamala

1Applied Engineering Department, Vignan’s Foundation for Science, Technology and Research University (VFSTRU), Vadlamudi-522213, Guntur, Andhra Pradesh, India.


The analysis of time series is based on the assumption that successive values in the data file represent consecutive measurements taken at equally spaced time intervals. There are two main goals of time series analysis: (a) identifying the nature of the phenomenon represented by the sequence of observations, and (b) forecasting or predicting future values of the time series variable. Both of these goals require that the pattern of observed time series data is identified and more or less formally described. Once the pattern is established, one can interpret and integrate it with other data (i.e., Use it in the theory of the investigated phenomenon, e.g., Seasonal commodity prices). Regardless of the depth of one’s understanding and the validity of our interpretation (theory) of the phenomenon, one can extrapolate the identified pattern to predict future events. This paper discusses about how to analyze time series data, what are its goals, types of time series data, and models available to analyze time series data.


Keywords: Time series, ARIMA, Box-Jenkins, rainfall, climate, hydrology.

Free Full-text PDF


How to cite this article:
Sirisha Adamala.Time Series Analysis: A Hydrological Prospective. American Journal of Scientific Research and Essays, 2016,1:4. DOI: 10.28933/adamala-ajsre-2016


References:
1. Arena VC, Mazumdar S, Zborowski JV, Talbott EO, He S, Chuang YH, Schwerha JJ (2006). A retrospective investigation of PM10 in ambient air and cardiopulmonary hospital admissions in Allegheny County, Pennsylvania: 1995–2000. Journal of Occupational and Environmental Medicine, 48:38-47.
2. Box GEP, Jenkins, GM (1976). Time series analysis: Forecasting and control. Prentice Hall, Upper Saddle River, NJ. pp. 575.
3. Carlson RF, McCormick JA, Watts DG (1970). Application of linear random models to four annual streamflow series. Water Resources Research, 6(4):1070-1078.
4. Chen HL, Rao AR (2002). Testing hydrologic time series for stationarity. Journal of Hydrologic Engineering, 7(2):129-136.
5. Chen HL, Rao AR (2003). Linearity analysis on stationary segments of hydrologic time series. Journal of Hydrology, 277(1-2):89-99.
6. Damle C, Yalcin A. (2007). Flood prediction using time series data mining. Journal of Hydrology, 333:305-316.
7. Dryden I, Markus L, Taylor C, Kovacs J (2005). Non-stationary spatio-temporal analysis of karst water levels. Journal of the Royal Statistical Society Series C, Applied Statistics, 54:673-690.
8. Hinich MJ (1982). Testing for gaussianity and linearity of a stationary time series. Journal of Time Series Analysis, 3(3):169-176.
9. Hipel KW, McLeod AI (1994). Time series modelling of water resources and environmental systems, Developments in water science, 45. Elsevier, New York.
10. Komornik J, Komornikova M, Mesiar R, Szokeova D, Szolgay J (2006). Comparison of forecasting performance of nonlinear models of hydrological time series. Physics and Chemistry of the Earth 31(18):1127-1145.
11. Salas JD (1993). Analysis and modeling of hydrologic time series. In: Maidment, D.R., (Ed.), McGraw Hill Handbook of Hydrology, pp. 19.5-19.9.
12. Sankarasubramanian A, Vogel RM (2002). Annual hydroclimatology of the United States. Water Resources Research, 38(6):1083-1095.
13. Srikanthan R, McMahon TA (2001). Stochastic generation of annual, monthly and daily climate data: A review. Hydrology and Earth System Sciences, 5(4):653-670.
14. Thompson R (1999). A time-series analysis of the changing seasonality of precipitation in the British Isles and neighbouring areas. Journal of Hydrology, 224:169-183.
15. Toth E, Brath A, Montanari A. (2000). Comparison of short-term rainfall prediction models for real-time flood forecasting. Journal of Hydrology, 239:132-147.
16. Wang WC, Chau KW, Cheng CT, Qiu L (2009). A Comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology, 374:294-306.
17. Young PC, Jakeman AJ, Post DA (1997). Recent advances in the data-based modelling and analysis of hydrological systems. Water Science Technology, 36(5):99-116.