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.

  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

  Autorentext

  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.

  Inhalt

  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 model2. Mathematical explanation of the Linear Regression model3. Worked out Python forecasting example with the Linear Regression model
  Chapter 11: The Decision Tree modelChapter Goal: Explains the Decision Tree model (intuitively, mathematically and give Python application with code and data set)No pages: 8Sub -Topics1. Understanding Decision Tree model2. Mathematical explanation of the Decision Tree model3. Worked out Python forecasting example with the Decision Tree model
  Chapter 12: The k-Nearest Neighbors VAR modelChapter Goal: explain the k-Nearest Neighbors (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding k-Nearest neighbors model2. Mathematical explanation of the k-Nearest neighbors model3. Worked out Python forecasting example with the k-Nearest neighbors model
  Chapter 13: The Random Forest ModelChapter Goal: explain the Random Forest (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding Random Forest model2. Mathematical explanation of the Random Forest model3. Worked out Python forecasting example with the Random Forest model
  Chapter 14: The XGBoost modelChapter Goal: Explains the XGBoost model (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1. Understanding XGBoost model2. Mathematical explanation of the XGBoost model3. Worked out Python forecasting example with the XGBoost model
  Chapter 15: The Neural Network modelChapter Goal: Explains the Neural Network model (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1. Understanding Neural Network model2. Mathematical explanation of the Neural Network model3. Worked out Python forecasting example with the Neural Network model
  Part V: Advanced Machine and Deep Learning Models
  Chapter 16: Recurrent Neural NetworksChapter Goal: Explains Recurrent Neural Networks (intuitively, mathematically and give python application with code and data set)No pages: 10S…