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: Time series data set as supervised learning problemFigure 1.9: Multivariate time series as supervised learning problemFigure 1.10: Univariate time series as multi-step supervised learning2 Chapter 2Figure 2.1: Time series forecasting templateFigure 2.2: Time series batch data processing architectureFigure 2.3: Real-time and streaming data processing architectureFigure 2.4: Understanding time series featuresFigure 2.5: A representation of data set splitsFigure 2.6: Machine learning model workflowFigure 2.7: Energy demand forecast end-to-end solution
3 Chapter 3Figure 3.1: Overview of Python libraries for time series dataFigure 3.2: Time series decomposition plot for the load data set (time range...Figure 3.3: Time series load value and trend decomposition plot
4 Chapter 4Figure 4.1: First order autoregression approachFigure 4.2: Second order autoregression approachFigure 4.3: Lag plot results from ts_data_load setFigure 4.4: Autocorrelation plot results from ts_data_load setFigure 4.5: Autocorrelation plot results from ts_data_load_subsetFigure 4.6: Autocorrelation plot results from ts_data_load set with plot_acf ...Figure 4.7: Autocorrelation plot results from ts_data_load_subset with plot_...Figure 4.8: Autocorrelation plot results from ts_data set with plot_pacf() f...Figure 4.9: Autocorrelation plot results from ts_data_load_subset with plot_...Figure 4.10: Forecast plot generated from ts_data set with plot_predict() fu...Figure 4.11: Visualizations generated from ts_data set with plot_diagnositcs...
5 Chapter 5Figure 5.1: Representation of a recurrent neural network unitFigure 5.2: Recurrent neural network architectureFigure 5.3: Back propagation process in recurrent neural networks to compute...Figure 5.4: Backpropagation process in recurrent neural networks to compute ...Figure 5.5: Transforming time series data into two tensorsFigure 5.6: Transforming time series data into two tensors for a univariate ...Figure 5.7: Ts_data_load train, validation, and test data sets plotFigure 5.8: Data preparation steps for the ts_data_load train data setFigure 5.9: Development of deep learning models in KerasFigure 5.10: Structure of a simple RNN model to be implemented with KerasFigure 5.11: Structure of a simple RNN model to be implemented with KerasFigure 5.12: Structure of a simple RNN model to be implemented with Keras fo...
6 Chapter 6Figure 6.1: The machine learning model workflowFigure 6.2: The modeling and scoring processFigure 6.3: First few rows of the energy data setFigure 6.4: Load data set plotFigure 6.5: Load data set plot of the first week of July 2014Figure 6.6: Web service deployment and consumptionFigure 6.7: Energy demand forecast end-to-end data flow
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