Machine Learning for Time Series Forecasting With Python

Includes index

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

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.