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Advanced Forecasting with Python
With State-of-the-Art-Models Including LSTMs, Facebook¿s Prophet, and Amazon¿s DeepAR
Taschenbuch von Joos Korstanje
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor

Joos is a data scientist, with over five years of industry experience in developing machine learning tools, of which a large part is forecasting models. He currently works at Disneyland Paris where he develops machine learning for a variety of tools. His experience in writing and teaching have motivated him to make this book on advanced forecasting with Python.

Zusammenfassung

Covers state-of-the-art-models including LSTMs, Facebook's Prophet, and Amazon's DeepAR

Includes an exhaustive overview of models relevant to forecasting

Provides intuitive explanations, mathematical background, and applied examples in Python for each of the 18 models covered

Inhaltsverzeichnis
PART I: Machine Learning for Forecasting
Chapter 1: Models for Forecasting
Chapter Goal: Explains the different categories of models that are relevant for forecasting in high level language
No pages: 10
Sub -Topics
1. Time series models
2. Supervised vs unsupervised models
3. Classification vs regression models
4. Univariate vs multivariate models
Chapter 2: Model Evaluation for Forecasting
Chapter Goal: Explains model evaluation with specific adaptations to keep in mind for forecasting
No pages: 15
Sub -Topics
1. Train test split
2. Cross validation for forecasting
3. Backtesting
PART II: Univariate Time Series Models
Chapter 3: The AR Model
Chapter Goal: explain the AR model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding AR model
2. Mathematical explanation of the AR model
3. Worked out Python forecasting example with the AR model
Chapter 4: The MA model
Chapter Goal: explain the MA model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding MA model
2. Mathematical explanation of the MA model
3. Worked out Python forecasting example with the MA model
Chapter 5: The ARMA model
Chapter Goal: explain the ARMA model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding ARMA model
2. Mathematical explanation of the ARMA model
3. Worked out Python forecasting example with the ARMA model
Chapter 6: The ARIMA model
Chapter Goal: Explains the ARIMA model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding ARIMA model
2. Mathematical explanation of the ARIMA model
3. Worked out Python forecasting example with the ARIMA model
Chapter 7: The SARIMA Model
Chapter Goal: Explains the SARIMA model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding SARIMA model
2. Mathematical explanation of the SARIMA model
3. Worked out Python forecasting example with the SARIMA model
PART III: Multivariate Time Series Models
Chapter 8: The VAR model
Chapter Goal: Explains the VAR model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding VAR model
2. Mathematical explanation of the VAR model
3. Worked out Python forecasting example with the VAR model
Chapter 9: The Bayesian VAR model
Chapter Goal: Explains the Bayesian VAR model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding Bayesian VAR model
2. Mathematical explanation of the Bayesian VAR model
3. Worked out Python forecasting example with the Bayesian VAR model
PART IV: Supervised Machine Learning Models
Chapter 10: The Linear Regression model
Chapter Goal: Explains the Linear Regression model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding Linear Regression model
2. Mathematical explanation of the Linear Regression model
3. Worked out Python forecasting example with the Linear Regression model
Chapter 11: The Decision Tree model
Chapter Goal: Explains the Decision Tree model (intuitively, mathematically and give Python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding Decision Tree model
2. Mathematical explanation of the Decision Tree model
3. Worked out Python forecasting example with the Decision Tree model
Chapter 12: The k-Nearest Neighbors VAR model
Chapter Goal: explain the k-Nearest Neighbors (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding k-Nearest neighbors model
2. Mathematical explanation of the k-Nearest neighbors model
3. Worked out Python forecasting example with the k-Nearest neighbors model
Chapter 13: The Random Forest Model
Chapter Goal: explain the Random Forest (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding Random Forest model
2. Mathematical explanation of the Random Forest model
3. Worked out Python forecasting example with the Random Forest model
Chapter 14: The XGBoost model
Chapter Goal: Explains the XGBoost model (intuitively, mathematically and give python application with code and data set)
No pages: 10
Sub -Topics
1. Understanding XGBoost model
2. Mathematical explanation of the XGBoost model
3. Worked out Python forecasting example with the XGBoost model
Chapter 15: The Neural Network model
Chapter Goal: Explains the Neural Network model (intuitively, mathematically and give python application with code and data set)
No pages: 10
Sub -Topics
1. Understanding Neural Network model
2. Mathematical explanation of the Neural Network model
3. Worked out Python forecasting example with the Neural Network model
Part V: Advanced Machine and Deep Learning Models
Chapter 16: Recurrent Neural Networks
Chapter Goal: Explains Recurrent Neural Networks (intuitively, mathematically and give python application with code and data set)
No pages: 10
Sub -Topics
1. Understanding Recurrent Neural Networks
2. Mathematical explanation of Recurrent Neural Networks
3. Worked out Python forecasting example with Recurrent Neural Networks
Chapter 17: LSTMs
Chapter Goal: Explains LSTMs (intuitively, mathematically and give python application with code and data set)
No pages: 10
Sub -Topics
1. Understanding LSTMs
2. Mathematical explanation of LSTMs
3. Worked out Python forecasting example with LSTMs
Chapter 18: Facebook's Prophet model
Chapter Goal: Explains Facebook's Prophet model (intuitively, mathematically and give Python application with code and data set)
No pages: 10
Sub -Topics
1. Understanding Facebook's Prophet model
2. Mathematical explanation of Facebook's Prophet model
3. Worked out Python forecasting example with Facebook's Prophet model
Chapter 19: Amazon's DeepAR Model
Chapter Goal: Explains Amazon's DeepAR model (intuitively, mathematically and give python application with code and data set)
No pages: 10
Sub -Topics
1. Understanding Amazon's DeepAR model
2. Mathematical explanation of Amazon's DeepAR model
3. Worked out Python forecasting example with Amazon's DeepAR model
Chapter 20: Deep State Space Models
Chapter Goal: Explains Deep State Space models (intuitively, mathematically and give Python application with code and data set)
No pages: 10
Sub -Topics
1. Understanding Deep State Space models
2. Mathematical explanation of Deep State Space models
3. Worked out Python forecasting example with Deep State Space models
Chapter 21: Model selection
Chapter Goal: Give elements to select the best model for a specific situation
No pages: 16
Sub -Topics
1. Benchmark scores vs understandability of models vs compute time
2. Black swan outlier problems
3. Automated retraining and updating of models
4. Conclusion
Details
Erscheinungsjahr: 2021
Genre: Importe, Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: xvii
296 S.
70 s/w Illustr.
36 farbige Illustr.
296 p. 106 illus.
36 illus. in color.
ISBN-13: 9781484271490
ISBN-10: 1484271491
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Korstanje, Joos
Auflage: 1st ed.
Hersteller: Apress
Apress L.P.
Verantwortliche Person für die EU: Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de
Maße: 254 x 178 x 18 mm
Von/Mit: Joos Korstanje
Erscheinungsdatum: 03.07.2021
Gewicht: 0,598 kg
Artikel-ID: 119980682
Über den Autor

Joos is a data scientist, with over five years of industry experience in developing machine learning tools, of which a large part is forecasting models. He currently works at Disneyland Paris where he develops machine learning for a variety of tools. His experience in writing and teaching have motivated him to make this book on advanced forecasting with Python.

Zusammenfassung

Covers state-of-the-art-models including LSTMs, Facebook's Prophet, and Amazon's DeepAR

Includes an exhaustive overview of models relevant to forecasting

Provides intuitive explanations, mathematical background, and applied examples in Python for each of the 18 models covered

Inhaltsverzeichnis
PART I: Machine Learning for Forecasting
Chapter 1: Models for Forecasting
Chapter Goal: Explains the different categories of models that are relevant for forecasting in high level language
No pages: 10
Sub -Topics
1. Time series models
2. Supervised vs unsupervised models
3. Classification vs regression models
4. Univariate vs multivariate models
Chapter 2: Model Evaluation for Forecasting
Chapter Goal: Explains model evaluation with specific adaptations to keep in mind for forecasting
No pages: 15
Sub -Topics
1. Train test split
2. Cross validation for forecasting
3. Backtesting
PART II: Univariate Time Series Models
Chapter 3: The AR Model
Chapter Goal: explain the AR model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding AR model
2. Mathematical explanation of the AR model
3. Worked out Python forecasting example with the AR model
Chapter 4: The MA model
Chapter Goal: explain the MA model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding MA model
2. Mathematical explanation of the MA model
3. Worked out Python forecasting example with the MA model
Chapter 5: The ARMA model
Chapter Goal: explain the ARMA model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding ARMA model
2. Mathematical explanation of the ARMA model
3. Worked out Python forecasting example with the ARMA model
Chapter 6: The ARIMA model
Chapter Goal: Explains the ARIMA model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding ARIMA model
2. Mathematical explanation of the ARIMA model
3. Worked out Python forecasting example with the ARIMA model
Chapter 7: The SARIMA Model
Chapter Goal: Explains the SARIMA model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding SARIMA model
2. Mathematical explanation of the SARIMA model
3. Worked out Python forecasting example with the SARIMA model
PART III: Multivariate Time Series Models
Chapter 8: The VAR model
Chapter Goal: Explains the VAR model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding VAR model
2. Mathematical explanation of the VAR model
3. Worked out Python forecasting example with the VAR model
Chapter 9: The Bayesian VAR model
Chapter Goal: Explains the Bayesian VAR model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding Bayesian VAR model
2. Mathematical explanation of the Bayesian VAR model
3. Worked out Python forecasting example with the Bayesian VAR model
PART IV: Supervised Machine Learning Models
Chapter 10: The Linear Regression model
Chapter Goal: Explains the Linear Regression model (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding Linear Regression model
2. Mathematical explanation of the Linear Regression model
3. Worked out Python forecasting example with the Linear Regression model
Chapter 11: The Decision Tree model
Chapter Goal: Explains the Decision Tree model (intuitively, mathematically and give Python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding Decision Tree model
2. Mathematical explanation of the Decision Tree model
3. Worked out Python forecasting example with the Decision Tree model
Chapter 12: The k-Nearest Neighbors VAR model
Chapter Goal: explain the k-Nearest Neighbors (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding k-Nearest neighbors model
2. Mathematical explanation of the k-Nearest neighbors model
3. Worked out Python forecasting example with the k-Nearest neighbors model
Chapter 13: The Random Forest Model
Chapter Goal: explain the Random Forest (intuitively, mathematically and give python application with code and data set)
No pages: 8
Sub -Topics
1. Understanding Random Forest model
2. Mathematical explanation of the Random Forest model
3. Worked out Python forecasting example with the Random Forest model
Chapter 14: The XGBoost model
Chapter Goal: Explains the XGBoost model (intuitively, mathematically and give python application with code and data set)
No pages: 10
Sub -Topics
1. Understanding XGBoost model
2. Mathematical explanation of the XGBoost model
3. Worked out Python forecasting example with the XGBoost model
Chapter 15: The Neural Network model
Chapter Goal: Explains the Neural Network model (intuitively, mathematically and give python application with code and data set)
No pages: 10
Sub -Topics
1. Understanding Neural Network model
2. Mathematical explanation of the Neural Network model
3. Worked out Python forecasting example with the Neural Network model
Part V: Advanced Machine and Deep Learning Models
Chapter 16: Recurrent Neural Networks
Chapter Goal: Explains Recurrent Neural Networks (intuitively, mathematically and give python application with code and data set)
No pages: 10
Sub -Topics
1. Understanding Recurrent Neural Networks
2. Mathematical explanation of Recurrent Neural Networks
3. Worked out Python forecasting example with Recurrent Neural Networks
Chapter 17: LSTMs
Chapter Goal: Explains LSTMs (intuitively, mathematically and give python application with code and data set)
No pages: 10
Sub -Topics
1. Understanding LSTMs
2. Mathematical explanation of LSTMs
3. Worked out Python forecasting example with LSTMs
Chapter 18: Facebook's Prophet model
Chapter Goal: Explains Facebook's Prophet model (intuitively, mathematically and give Python application with code and data set)
No pages: 10
Sub -Topics
1. Understanding Facebook's Prophet model
2. Mathematical explanation of Facebook's Prophet model
3. Worked out Python forecasting example with Facebook's Prophet model
Chapter 19: Amazon's DeepAR Model
Chapter Goal: Explains Amazon's DeepAR model (intuitively, mathematically and give python application with code and data set)
No pages: 10
Sub -Topics
1. Understanding Amazon's DeepAR model
2. Mathematical explanation of Amazon's DeepAR model
3. Worked out Python forecasting example with Amazon's DeepAR model
Chapter 20: Deep State Space Models
Chapter Goal: Explains Deep State Space models (intuitively, mathematically and give Python application with code and data set)
No pages: 10
Sub -Topics
1. Understanding Deep State Space models
2. Mathematical explanation of Deep State Space models
3. Worked out Python forecasting example with Deep State Space models
Chapter 21: Model selection
Chapter Goal: Give elements to select the best model for a specific situation
No pages: 16
Sub -Topics
1. Benchmark scores vs understandability of models vs compute time
2. Black swan outlier problems
3. Automated retraining and updating of models
4. Conclusion
Details
Erscheinungsjahr: 2021
Genre: Importe, Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: xvii
296 S.
70 s/w Illustr.
36 farbige Illustr.
296 p. 106 illus.
36 illus. in color.
ISBN-13: 9781484271490
ISBN-10: 1484271491
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Korstanje, Joos
Auflage: 1st ed.
Hersteller: Apress
Apress L.P.
Verantwortliche Person für die EU: Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de
Maße: 254 x 178 x 18 mm
Von/Mit: Joos Korstanje
Erscheinungsdatum: 03.07.2021
Gewicht: 0,598 kg
Artikel-ID: 119980682
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