國立虎尾科技大學 |

Advanced Forecasting with Python = With State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Advanced Forecasting with Python/ by Joos Korstanje.
Reminder of title:	With State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR /
Author:	Korstanje, Joos.
Description:	XVII, 296 p. 106 illus., 36 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-7150-6
ISBN:	9781484271506

Advanced Forecasting with Python = With State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR /
Korstanje, Joos.

Advanced Forecasting with PythonWith State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR /[electronic resource] :by Joos Korstanje. - 1st ed. 2021. - XVII, 296 p. 106 illus., 36 illus. in color.online resource.

Chapter 1: Models for Forecasting -- Chapter 2: Model Evaluation for Forecasting -- Chapter 3: The AR Model -- Chapter 4: The MA model -- Chapter 5: The ARMA model -- Chapter 6: The ARIMA model -- Chapter 7: The SARIMA Model -- Chapter 8: The VAR model -- Chapter 9: The Bayesian VAR model -- Chapter 10: The Linear Regression model -- Chapter 11: The Decision Tree model -- Chapter 12: The k-Nearest Neighbors VAR model -- Chapter 13: The Random Forest Model -- Chapter 14: The XGBoost model -- Chapter 15: The Neural Network model -- Chapter 16: Recurrent Neural Networks -- Chapter 17: LSTMs -- Chapter 18: Facebook’s Prophet model -- Chapter 19: Amazon’s DeepAR Model -- Chapter 20: Deep State Space Models -- Chapter 21: Model selection.

Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook’s open-source Prophet model, and Amazon’s DeepAR model. Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in depth, with an intuitive simple explanation of the model, a mathematical transcription of the idea, and Python code that applies the model to an example data set. Reading this book will add a competitive edge to your current forecasting skillset. The book is also adapted to those who have recently started working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models. You will: Carry out forecasting with Python Mathematically and intuitively understand traditional forecasting models and state-of-the-art machine learning techniques Gain the basics of forecasting and machine learning, including evaluation of models, cross-validation, and back testing Select the right model for the right use case.

ISBN: 9781484271506

Standard No.: 10.1007/978-1-4842-7150-6doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5-.7

Dewey Class. No.: 006.31

Advanced Forecasting with Python = With State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR /
LDR:03906nam a22003975i 4500 001 1046790
003 DE-He213
005 20210705072246.0
007 cr nn 008mamaa
008 220103s2021 xxu| s |||| 0|eng d
020 $a 9781484271506 $9 978-1-4842-7150-6
024 7 $a 10.1007/978-1-4842-7150-6 $2 doi
035 $a 978-1-4842-7150-6
050 4 $a Q325.5-.7
050 4 $a TK7882.P3
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
100 1 $a Korstanje, Joos. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1350380
245 1 0 $a Advanced Forecasting with Python $h [electronic resource] : $b With State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR / $c by Joos Korstanje.
250 $a 1st ed. 2021.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a XVII, 296 p. 106 illus., 36 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Chapter 1: Models for Forecasting -- Chapter 2: Model Evaluation for Forecasting -- Chapter 3: The AR Model -- Chapter 4: The MA model -- Chapter 5: The ARMA model -- Chapter 6: The ARIMA model -- Chapter 7: The SARIMA Model -- Chapter 8: The VAR model -- Chapter 9: The Bayesian VAR model -- Chapter 10: The Linear Regression model -- Chapter 11: The Decision Tree model -- Chapter 12: The k-Nearest Neighbors VAR model -- Chapter 13: The Random Forest Model -- Chapter 14: The XGBoost model -- Chapter 15: The Neural Network model -- Chapter 16: Recurrent Neural Networks -- Chapter 17: LSTMs -- Chapter 18: Facebook’s Prophet model -- Chapter 19: Amazon’s DeepAR Model -- Chapter 20: Deep State Space Models -- Chapter 21: Model selection.
520 $a Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook’s open-source Prophet model, and Amazon’s DeepAR model. Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in depth, with an intuitive simple explanation of the model, a mathematical transcription of the idea, and Python code that applies the model to an example data set. Reading this book will add a competitive edge to your current forecasting skillset. The book is also adapted to those who have recently started working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models. You will: Carry out forecasting with Python Mathematically and intuitively understand traditional forecasting models and state-of-the-art machine learning techniques Gain the basics of forecasting and machine learning, including evaluation of models, cross-validation, and back testing Select the right model for the right use case.
650 0 $a Machine learning. $3 561253
650 0 $a Python (Computer program language). $3 1127623
650 1 4 $a Machine Learning. $3 1137723
650 2 4 $a Python. $3 1115944
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484271490
776 0 8 $i Printed edition: $z 9781484271513
856 4 0 $u https://doi.org/10.1007/978-1-4842-7150-6
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)