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عنوان
Time Series Algorithms Recipes :

پدید آورنده
Akshay R. Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V. Adithya Krishnan.,Kulkarni, Akshay R.,

موضوع
Time-series analysis,Time-series analysis,Machine learning,Python (Computer program language),Computer programs.,Data processing.,Computer programs.

رده
HA30
.
3
.
K85
2023

کتابخانه
Central Library, Center of Documentation and Supply of Scientific Resources

محل استقرار
استان: East Azarbaijan ـ شهر:

Central Library, Center of Documentation and Supply of Scientific Resources

تماس با کتابخانه : 04133443834

INTERNATIONAL STANDARD BOOK NUMBER

Qualification
(electronic bk.)
Qualification
(electronic bk.)
(Number (ISBN
9781484289785
(Number (ISBN
1484289781
Erroneous ISBN
9781484289778
Erroneous ISBN
1484289773

INTERNATIONAL STANDARD MUSIC NUMBER

(Number (ISMN
10.1007/978-1-4842-8978-5

NATIONAL BIBLIOGRAPHY NUMBER

Number
15130

OTHER SYSTEM CONTROL NUMBERS

System Control Number
(OCoLC)1356572959
Cancelled or Invalid Control Number
(OCoLC)1356982902

LANGUAGE OF THE ITEM

.Language of Text, Soundtrack etc
انگلیسی

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Time Series Algorithms Recipes :
Other Title Information
Implement Machine Learning and Deep Learning Techniques with Python /
First Statement of Responsibility
Akshay R. Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V. Adithya Krishnan.

.PUBLICATION, DISTRIBUTION, ETC

Place of Publication, Distribution, etc.
New York, NY :
Name of Publisher, Distributor, etc.
Apress L. P.,
Date of Publication, Distribution, etc.
[2023]

PHYSICAL DESCRIPTION

Specific Material Designation and Extent of Item
174p.
Other Physical Details
illustrations.

NOTES PERTAINING TO BINDING AND AVAILABILITY

Text of Note
Available to OhioLINK libraries.

CONTENTS NOTE

Text of Note
Chapter 1: Getting Started with Time Series -- Chapter 2: Statistical Univariate Modelling -- Chapter 3: Statistical Multivariate Modelling -- Chapter 4: Machine Learning Regression-Based Forecasting -- Chapter 5: Forecasting Using Deep Learning.
0

SUMMARY OR ABSTRACT

Text of Note
This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will Learn Implement various techniques in time series analysis using Python. Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecasting Forecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.

ACQUISITION INFORMATION NOTE

Source for Acquisition/Subscription Address
O'Reilly Media
Stock Number
9781484289785

OTHER EDITION IN ANOTHER MEDIUM

Author
Kulkarni, Akshay R.
Place of Publication
Berkeley, CA : Apress L. P.,c2023
Title
Time Series Algorithms Recipes
International Standard Book Number
9781484289778.

TOPICAL NAME USED AS SUBJECT

Entry Element
Time-series analysis
Entry Element
Time-series analysis
Entry Element
Machine learning
Entry Element
Python (Computer program language)
Topical Subdivision
Computer programs.
Topical Subdivision
Data processing.
Topical Subdivision
Computer programs.

(SUBJECT CATEGORY (Provisional

Subject Category Subdivision Code
UYQM
Subject Category Subdivision Code
COM004000
Subject Category Subdivision Code
UYQM
System Code
bicssc
System Code
bisacsh
System Code
thema

DEWEY DECIMAL CLASSIFICATION

Number
006
.
31
Edition
23/eng/20230105

LIBRARY OF CONGRESS CLASSIFICATION

Class number
HA30
.
3
Book number
.
K85
2023

PERSONAL NAME - PRIMARY RESPONSIBILITY

Entry Element
Kulkarni, Akshay R.,

PERSONAL NAME - SECONDARY RESPONSIBILITY

Entry Element
Shivananda, Adarsha,
Entry Element
Kulkarni, Anoosh,
Entry Element
Krishnan, V. Adithya,

CORPORATE BODY NAME - SECONDARY RESPONSIBILITY

Entry Element
Ohio Library and Information Network.

ORIGINATING SOURCE

Agency
کتابخانه مرکزی و مرکز اطلاع رسانی دانشگاه
Date of Transaction
20231218061624.0
Cataloguing Rules (Descriptive Conventions))
rda

ELECTRONIC LOCATION AND ACCESS

Electronic name
Implement Machine Learning and Deep Learning Techniques with Python-Apress (20.pdf
Uniform Resource Identifier
https://rave.ohiolink.edu/ebooks/ebc2/9781484289785
Uniform Resource Identifier
https://link.springer.com/10.1007/978-1-4842-8978-5
Uniform Resource Identifier
http://proxy.ohiolink.edu:9099/login?url=https://link.springer.com/10.1007/978-1-4842-8978-5
Uniform Resource Identifier
https://learning.oreilly.com/library/view/~/9781484289785/?ar
Public note
Connect to resource
Public note
Connect to resource
Public note
Connect to resource (off-campus)
Public note
Connect to resource

BL
270410

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