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Table of Contents
1 Cover
3 Introduction What Does This Book Cover? Reader Support for This Book
4 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
5 CHAPTER 2: How to Design an End-to-End Time Series Forecasting Solution on the Cloud Time Series Forecasting Template An Overview of Demand Forecasting Modeling Techniques Use Case: Demand Forecasting Conclusion
6 CHAPTER 3: Time Series Data Preparation Python for Time Series Data Time Series Exploration and Understanding Time Series Feature Engineering Conclusion
7 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
8 CHAPTER 5: Introduction to Neural Networks for Time Series Forecasting Reasons to Add Deep Learning to Your Time Series Toolkit Recurrent Neural Networks for Time Series Forecasting How to Develop GRUs and LSTMs for Time Series Forecasting Conclusion
9 CHAPTER 6: Model Deployment for Time Series Forecasting Experimental Set Up and Introduction to Azure Machine Learning SDK for Python Machine Learning Model Deployment Solution Architecture for Time Series Forecasting with Deployment Examples Conclusion
10 References
11 Index
12 Copyright
List of Tables
1 Chapter 2Table 2.1: Examples of compute targets that can be used to host your web servi...Table 2.2: Short-term versus long-term predictions
2 Chapter 3Table 3.1: Four general time-related concepts supported in pandasTable 3.2: Comparison of strftime() and strptime() functionalitiesTable 3.3: Date and time properties from Timestamp and DatetimeIndexTable 3.4: Offset aliases supported in Python
3 Chapter 4Table 4.1: pandas.plotting.lag_plot API reference and descriptionTable 4.2: pandas.plotting.lag_plot API reference and descriptionTable 4.3: Autoregressive class in statsmodelsTable 4.4: Definition and parameters of autoregressive class in statsmodelsTable 4.5: Autoregressive moving average in statsmodelsTable 4.6: Definition and parameters of autoregressive moving average class in...Table 4.7: Seasonal auto regressive integrated moving average with exogenous f...Table 4.8: Definition and parameters of seasonal auto regressive integrated mo...Table 4.9: Automated ML parameters to be configured with the AutoML Config cla...
4 Chapter 5Table 5.1: Key differences between machine learning and deep learning
5 Chapter 6Table 6.1: Creating a deployment configuration for each compute target
List of Illustrations
1 Chapter 1Figure 1.1: Example of time series forecasting applied to the energy load us...Figure 1.2: Machine learning data set versus time series data setFigure 1.3: Difference between time series analysis historical input data an...Figure 1.4: Components of time seriesFigure 1.5: Differences between cyclic variations versus seasonal variations...Figure 1.6: Actual representation of time series componentsFigure 1.7: Handling missing dataFigure 1.8: