Advanced Forecasting with Python by Joos Korstanje
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Advanced Forecasting with Python

Advanced Forecasting with Python


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About the Book

PART I: Machine Learning for Forecasting
Chapter 1: Models for ForecastingChapter Goal: Explains the different categories of models that are relevant for forecasting in high level languageNo pages: 10Sub -Topics1. Time series models2. Supervised vs unsupervised models3. Classification vs regression models4. Univariate vs multivariate models
Chapter 2: Model Evaluation for ForecastingChapter Goal: Explains model evaluation with specific adaptations to keep in mind for forecastingNo pages: 15Sub -Topics1. Train test split2. Cross validation for forecasting3. Backtesting
PART II: Univariate Time Series Models
Chapter 3: The AR ModelChapter Goal: explain the AR model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding AR model2. Mathematical explanation of the AR model3. Worked out Python forecasting example with the AR model
Chapter 4: The MA modelChapter Goal: explain the MA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding MA model2. Mathematical explanation of the MA model3. Worked out Python forecasting example with the MA model
Chapter 5: The ARMA modelChapter Goal: explain the ARMA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding ARMA model2. Mathematical explanation of the ARMA model3. Worked out Python forecasting example with the ARMA model
Chapter 6: The ARIMA modelChapter Goal: Explains the ARIMA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding ARIMA model2. Mathematical explanation of the ARIMA model3. Worked out Python forecasting example with the ARIMA model
Chapter 7: The SARIMA ModelChapter Goal: Explains the SARIMA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding SARIMA model2. Mathematical explanation of the SARIMA model3. Worked out Python forecasting example with the SARIMA model
PART III: Multivariate Time Series Models
Chapter 8: The VAR modelChapter Goal: Explains the VAR model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding VAR model2. Mathematical explanation of the VAR model3. Worked out Python forecasting example with the VAR model
Chapter 9: The Bayesian VAR modelChapter Goal: Explains the Bayesian VAR model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding Bayesian VAR model2. Mathematical explanation of the Bayesian VAR model3. Worked out Python forecasting example with the Bayesian VAR model
PART IV: Supervised Machine Learning Models
Chapter 10: The Linear Regression modelChapter Goal: Explains the Linear Regression model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding Linear Regression model
About the Author:

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.



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Product Details
  • ISBN-13: 9781484271490
  • Publisher: Apress
  • Publisher Imprint: Apress
  • Height: 254 mm
  • No of Pages: 296
  • Spine Width: 17 mm
  • Weight: 648 gr
  • ISBN-10: 1484271491
  • Publisher Date: 18 Oct 2021
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Sub Title: With State-Of-The-Art-Models Including Lstms, Facebook's Prophet, and Amazon's Deepar
  • Width: 178 mm


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