MODELING OF TIME ROWS USING THE LEAST SQUARES METHOD

Ilhomjon Qodirov

Authors

            Nizamitdinov A.I.–Doctor of Philosophy, Department of Programming and Information Systems, Polytechnic Institute of Tajik Technical University, Khujand, Republic of Tajikistan, ahlidin@gmail.com.

           Lashena T.V. – Master student of specialty 400102, Polytechnic Institute of Tajik Technical University, Khujand, Republic of Tajikistan, lashena_t@mail.ru.

 Annotation

       For modelling of time series usually using group of approaches are determining modelling, stochastic modelling and state space modelling. This article considered modelling of time series using determining type of modelling missed values of time series data using least squares approach. The functions often used to fit real time series dataset will create discrepancies or data set generated from experimental measurements are subject to error. In such cases is usually used the method of least squares.Based on statistical theory, this approach constructs multivariate equation which with major probability can fit to real values of time series. The main objective in constructing models of time series is assessment of missing values using least squares approach. As an example of this approach it is considered simulated data with 3 missing values. It is conducted step-by-step construction of model for assessment missed values of time series.

Key words

    time series, assessment, deterministic modelling, stochastic modelling, least squares approach, approximation

Language

english

Type

technical

Year

2019

Page

27

References

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Publicaton date

2023-09-23