5.1. Introduction to large-scale time series and importing into Python programming environment
5.2. Data preprocessing
5.2.1. Detection and correction of missing values in multivariate time series
5.2.2. Plotting multivariate time series data
5.2.3. Exploring downsampling and implementing it in the Python programming environment
5.2.4. Transforming single-objective multivariate time series into a supervised learning problem
5.2.5. Normalizing multivariate time series for single-objective forecasting problems
5.3. Single-objective multivariate time series prediction with multiple time steps using deep neural networks (RNN, GRU, LSTM, CNN, and MLP)
5.3.1. Single-objective multivariate time series prediction with multiple time steps using RNN deep neural network
5.3.2. Single-objective multivariate time series prediction with multiple time steps using GRU deep neural network
5.3.3. Single-objective multivariate time series prediction with multiple time steps using LSTM deep neural network
5.3.4. Single-objective multivariate time series prediction with multiple time steps using CNN deep neural network
5.3.5. Multivariate time series prediction with multiple time steps using a single objective MLP neural network
5.4. Comparison between the predictions resulted from various neural networks