Machine Learning for Time Series Forecasting With Python

Learn how to apply the principles of machine learning to time series modeling with this indispensable resource
 
Machine Learning for Time Series Forecasting with Python 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.
 
Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting.
 
Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to:
* Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality
* Prepare time series data for modeling
* Evaluate time series forecasting models' performance and accuracy
* Understand when to use neural networks instead of traditional time series models in time series forecasting
 
Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts.
 
Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.

Sommario

Acknowledgments vii
 
Introduction xv
 
Chapter 1 Overview of Time Series Forecasting 1
 
Flavors of Machine Learning for Time Series Forecasting 3
 
Supervised Learning for Time Series Forecasting 14
 
Python for Time Series Forecasting 21
 
Experimental Setup for Time Series Forecasting 24
 
Conclusion 26
 
Chapter 2 How to Design an End-to-End Time Series Forecasting Solution on the Cloud 29
 
Time Series Forecasting Template 31
 
Business Understanding and Performance Metrics 33
 
Data Ingestion 36
 
Data Exploration and Understanding 39
 
Data Pre-processing and Feature Engineering 40
 
Modeling Building and Selection 42
 
An Overview of Demand Forecasting Modeling Techniques 44
 
Model Evaluation 46
 
Model Deployment 48
 
Forecasting Solution Acceptance 53
 
Use Case: Demand Forecasting 54
 
Conclusion 58
 
Chapter 3 Time Series Data Preparation 61
 
Python for Time Series Data 62
 
Common Data Preparation Operations for Time Series 65
 
Time stamps vs. Periods 66
 
Converting to Timestamps 69
 
Providing a Format Argument 70
 
Indexing 71
 
Time/Date Components 76
 
Frequency Conversion 78
 
Time Series Exploration and Understanding 79
 
How to Get Started with Time Series Data Analysis 79
 
Data Cleaning of Missing Values in the Time Series 84
 
Time Series Data Normalization and Standardization 86
 
Time Series Feature Engineering 89
 
Date Time Features 90
 
Lag Features and Window Features 92
 
Rolling Window Statistics 95
 
Expanding Window Statistics 97
 
Conclusion 98
 
Chapter 4 Introduction to Autoregressive and Automated Methods for Time Series Forecasting 101
 
Autoregression 102
 
Moving Average 119
 
Autoregressive Moving Average 120
 
Autoregressive Integrated Moving Average 122
 
Automated Machine Learning 129
 
Conclusion 136
 
Chapter 5 Introduction to Neural Networks for Time Series Forecasting 137
 
Reasons to Add Deep Learning to Your Time Series Toolkit 138
 
Deep Learning Neural Networks Are Capable of Automatically Learning and Extracting Features from Raw and Imperfect Data 140
 
Deep Learning Supports Multiple Inputs and Outputs 142
 
Recurrent Neural Networks Are Good at Extracting Patterns from Input Data 143
 
Recurrent Neural Networks for Time Series Forecasting 144
 
Recurrent Neural Networks 145
 
Long Short-Term Memory 147
 
Gated Recurrent Unit 148
 
How to Prepare Time Series Data for LSTMs and GRUs 150
 
How to Develop GRUs and LSTMs for Time Series Forecasting 154
 
Keras 155
 
TensorFlow 156
 
Univariate Models 156
 
Multivariate Models 160
 
Conclusion 164
 
Chapter 6 Model Deployment for Time Series Forecasting 167
 
Experimental Set Up and Introduction to Azure Machine Learning SDK for Python 168
 
Workspace 169
 
Experiment 169
 
Run 169
 
Model 170
 
Compute Target, RunConfiguration, and ScriptRun Config 171
 
Image and Webservice 172
 
Machine Learning Model Deployment 173
 
How to Select the Right Tools to Succeed with Model Deployment 175
 
Solution Architecture for Time Series Forecasting with Deployment Examples 177
 
Train and Deploy an ARIMA Model 179
 
Configure the Workspace 182
 
Create an Experiment 183
 
Create or Attach a Compute Clus

Info autore

FRANCESCA LAZZERI is an accomplished economist who works with machine learning, artificial intelligence, and applied econometrics. She works at Microsoft as a data scientist and machine learning scientist to develop a portfolio of machine learning services. She is a sought-after speaker and has given popular talks at AI conferences and academic seminars at Berkeley, Harvard, and MIT.

Riassunto

Learn how to apply the principles of machine learning to time series modeling with this indispensable resource

Machine Learning for Time Series Forecasting with Python 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.

Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting.

Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to:
* Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality
* Prepare time series data for modeling
* Evaluate time series forecasting models' performance and accuracy
* Understand when to use neural networks instead of traditional time series models in time series forecasting

Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts.

Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.

Dettagli sul prodotto

• Autori: F Lazzeri, Francesca Lazzeri
• Editore: Wiley, John and Sons Ltd
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 30.11.2020
• EAN: 9781119682363
• ISBN: 978-1-119-68236-3
• Pagine: 224
• Categorie: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Software applicativo; Statistik, Informatik, Python (Programmiersprache), Data Mining, Maschinelles Lernen, Statistics, computer science, Zeitreihen, Zeitreihe, Time Series, Data Mining Statistics, Data Mining & Knowledge Discovery, Data Mining u. Knowledge Discovery