Random Processes and Time Series

František Štulajter ()
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František Štulajter: Comenius University, Department of Statistics, FMFI UK

Chapter 2 in Predictions in Time Series Using Regression Models, 2002, pp 51-71 from Springer

Abstract: Abstract Random processes and their special types, time series, are used in many fields of human life. They serve as models for real processes which are of random character, that is for processes randomly changing in time. As an example we can give the changes in temperature of air observed in some meteorological laboratory, changes level of a river, in the consumption of electrical energy in some town observed continuously during some time interval, or the heart action of a patient recorded by his ECG, some other numerical parameters of a patient observed during his stay in hospital. To this type also belongs production of some company, recorded by days or months. Another kind of random process can be represented by the measurements of the diameter of a shaft along its length, or by the measurement of consumption of a gasoline by a car at different speeds. In these last two examples we can substitute the real time by some other “time” parameter, for example, by the speed of the car.

Keywords: Time Series; Spectral Density; Random Process; Covariance Function; Autocovariance Function (search for similar items in EconPapers)
Date: 2002
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DOI: 10.1007/978-1-4757-3629-8_2

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