Application of regression models in predicting time series (Record no. 1433044)

MARC details
000 -LEADER
fixed length control field 01912nam a2200241 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250630164101.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250627b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781835351031
040 ## - CATALOGING SOURCE
Original cataloging agency CSL
Transcribing agency CSL
041 ## - LANGUAGE CODE
Source of code eng
Language code of text/sound track or separate title eng
084 ## - COLON CLASSIFICATION NUMBER
Classification number B28 R4
Assigning agency CSL
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Yoruk, Esin
9 (RLIN) 814777
245 ## - TITLE STATEMENT
Title Application of regression models in predicting time series
Statement of responsibility, etc. / by Esin, Yoruk
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. London :
Name of publisher, distributor, etc. EDTECH PRESS,
Date of publication, distribution, etc. 2025.
300 ## - PHYSICAL DESCRIPTION
Extent xii, 285p.
Other physical details : ill.
Dimensions ; 25 cm.
500 ## - GENERAL NOTE
General note Includes Bibliography
520 ## - SUMMARY, ETC.
Summary, etc. For 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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Forecasting Techniques and Methods
9 (RLIN) 814778
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Time-series analysis
9 (RLIN) 814779
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Econometric models
9 (RLIN) 814780
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Estimation
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Colon Classification (CC)
Suppress in OPAC No
Koha item type Textual
Classification part B28 R4
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
    Colon Classification (CC)     Central Science Library Central Science Library 2024-10-30 Indica Publishers & Distributors Pvt. Ltd.   B28 R4 SL1656007 2025-06-27 2025-06-27 Textual