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. Les mer
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Vår pris: 609,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 7 virkedager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

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.





Fakta

Innholdsfortegnelse

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 Cluster 184


Upload the Data to Azure 184


Create an Estimator 188


Submit the Job to the Remote Cluster 188


Register the Model 189


Deployment 189


Define Your Entry Script and Dependencies 190


Automatic Schema Generation 191


Conclusion 196


References 197


Index 199

Om forfatteren

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.