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Advanced Forecasting with Python: With State-of-the-Art-Models Including LSTMs,
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Caractéristiques de l'objet

État
Neuf: Livre neuf, n'ayant jamais été lu ni utilisé, en parfait état, sans pages manquantes ni ...
ISBN-13
9781484271490
Book Title
Advanced Forecasting with Python
ISBN
9781484271490
Subject Area
Mathematics, Computers, Science
Publication Name
Advanced Forecasting with Python : With State-Of-the-Art-Models Including LSTMs, Facebook's Prophet, and Amazon's DeepAR
Publisher
Apress L. P.
Item Length
10 in
Subject
Probability & Statistics / General, Intelligence (Ai) & Semantics, General, Programming Languages / Python
Publication Year
2021
Type
Textbook
Format
Trade Paperback
Language
English
Author
JOOS Korstanje
Item Weight
21.3 Oz
Item Width
7 in
Number of Pages
Xvii, 296 Pages

À propos de ce produit

Product Identifiers

Publisher
Apress L. P.
ISBN-10
1484271491
ISBN-13
9781484271490
eBay Product ID (ePID)
2321443703

Product Key Features

Number of Pages
Xvii, 296 Pages
Language
English
Publication Name
Advanced Forecasting with Python : With State-Of-the-Art-Models Including LSTMs, Facebook's Prophet, and Amazon's DeepAR
Subject
Probability & Statistics / General, Intelligence (Ai) & Semantics, General, Programming Languages / Python
Publication Year
2021
Type
Textbook
Subject Area
Mathematics, Computers, Science
Author
JOOS Korstanje
Format
Trade Paperback

Dimensions

Item Weight
21.3 Oz
Item Length
10 in
Item Width
7 in

Additional Product Features

Dewey Edition
23
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
006.31
Table Of Content
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.
Synopsis
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. What You Will Learn 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 Who This Book Is For The advanced nature of the later chapters makes the book relevant for applied experts working in the domain of forecasting, as the models covered have been published only recently. Experts working in the domain will want to update their skills as traditional models are regularly being outperformed by newer models., 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 ofthe 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. What You Will Learn 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 Who This Book Is For The advanced nature of the later chapters makes the book relevant for appliedexperts working in the domain of forecasting, as the models covered have been published only recently. Experts working in the domain will want to update their skills as traditional models are regularly being outperformed by newer models.
LC Classification Number
Q325.5-.7

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