Part 2: Building and Deploying a Forecasting Model

In this part, we enter the world of predictive modeling applied to time series. We proceed with practical examples in a crescendo of technical difficulty, starting from statistics-based and finishing with machine learning-based forecasting. The following are the chapters included in this part:

• Chapter 6, Humidity Forecasting with Classical Methods
• Chapter 7, Forecasting the Temperature with ARIMA and SARIMA Models
• Chapter 8, Audio Signal Classification with an FFT and a Gradient Boosted Forest
• Chapter 9, Training and Deploying a Neural Network to Predict Glucose Levels
• Chapter 10, Predicting Energy Demand with an LSTM Model
• Chapter 11, Anomaly Detection – Predicting Failure with No Failure Examples