Machine Learning for Time Series Forecasting with Python

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This book is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. --

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Lazzeri, Francesca
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2020.
Temas:
Machine learning.
Python (Computer program language)
Apprentissage automatique.
Python (Langage de programmation)
Machine learning
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • About the Author
  • About the Technical Editor
  • Acknowledgments
  • Contents at a Glance
  • Contents
  • Introduction
  • Chapter 1 Overview of Time Series Forecasting
  • Flavors of Machine Learning for Time Series Forecasting
  • Supervised Learning for Time Series Forecasting
  • Python for Time Series Forecasting
  • Experimental Setup for Time Series Forecasting
  • Conclusion
  • Chapter 2 How to Design an End-to-End Time Series Forecasting Solution on the Cloud
  • Time Series Forecasting Template
  • Business Understanding and Performance Metrics
  • Data Ingestion
  • Data Exploration and Understanding
  • Data Pre-processing and Feature Engineering
  • Modeling Building and Selection
  • An Overview of Demand Forecasting Modeling Techniques
  • Model Evaluation
  • Model Deployment
  • Forecasting Solution Acceptance
  • Use Case: Demand Forecasting
  • Conclusion
  • Chapter 3 Time Series Data Preparation
  • Python for Time Series Data
  • Common Data Preparation Operations for Time Series
  • Time stamps vs. Periods
  • Converting to Time stamps
  • Providing a Format Argument
  • Indexing
  • Time/Date Components
  • Frequency Conversion
  • Time Series Exploration and Understanding
  • How to Get Started with Time Series Data Analysis
  • Data Cleaning of Missing Values in the Time Series
  • Time Series Data Normalization and Standardization
  • Time Series Feature Engineering
  • Date Time Features
  • Lag Features and Window Features
  • Rolling Window Statistics
  • Expanding Window Statistics
  • Conclusion
  • Chapter 4 Introduction to Autoregressive and Automated Methods for Time Series Forecasting
  • Autoregression
  • Moving Average
  • Autoregressive Moving Average
  • Autoregressive Integrated Moving Average
  • Automated Machine Learning
  • Conclusion
  • Chapter 5 Introduction to Neural Networks for Time Series Forecasting
  • Reasons to Add Deep Learning to Your Time Series Toolkit
  • Deep Learning Neural Networks Are Capable of Automatically Learning and Extracting Features from Raw and Imperfect Data
  • Deep Learning Supports Multiple Inputs and Outputs
  • Recurrent Neural Networks Are Good at Extracting Patterns from Input Data
  • Recurrent Neural Networks for Time Series Forecasting
  • Recurrent Neural Networks
  • Long Short-Term Memory
  • Gated Recurrent Unit
  • How to Prepare Time Series Data for LSTMs and GRUs
  • How to Develop GRUs and LSTMs for Time Series Forecasting
  • Keras
  • TensorFlow
  • Univariate Models
  • Multivariate Models
  • Conclusion
  • Chapter 6 Model Deployment for Time Series Forecasting
  • Experimental Set Up and Introduction to Azure Machine Learning SDK for Python
  • Workspace
  • Experiment
  • Run
  • Model
  • Compute Target, RunConfiguration, and ScriptRunConfig
  • Image and Webservice
  • Machine Learning Model Deployment
  • How to Select the Right Tools to Succeed with Model Deployment

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