000			01912nam a2200241 4500
005			20250630164101.0
008			250627b |||||||| |||| 00| 0 eng d
020			_a9781835351031
040			_aCSL _cCSL
041			_2eng _aeng
084			_aB28 R4 _qCSL
100			_aYoruk, Esin _9814777
245			_aApplication of regression models in predicting time series _c/ by Esin, Yoruk
260			_aLondon : _bEDTECH PRESS, _c2025.
300			_axii, 285p. _b: ill. _c; 25 cm.
500			_aIncludes Bibliography
520			_aFor more than a century, regression techniques have been an essential component of time arrangement research. Recent developments have allowed for genuine gains in areas like non-constant information where a direct model isn't applicable. This book introduces the reader to newer advancements and a wider selection of regression models and techniques for analysing timetables. Regression Models for Time Series Analysis is accessible to everyone who is familiar with the basic modern concepts of factual deduction and provides a really important investigation of recent measurable improvements. The crucial class of models known as summed up straight models (GLM), which provides, under some circumstances, a bound-together regression hypothesis appropriate for continuous, all-out, and check information, is among them. The designers purposefully extend the GLM methodology to time arrangements where the key information and covariate information are both random and stochastically dependent. They familiarise readers with various regression models developed over the past thirty years or so and consolidate earlier and more recent findings about state space models.
650			_aForecasting Techniques and Methods _9814778
650			_aTime-series analysis _9814779
650			_aEconometric models _9814780
650			_aEstimation
942			_2CC _n0 _cTEXL _hB28 R4
999			_c1433044 _d1433044