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Part 1. Time waits for no one
1 Understanding time series forecasting
1.2 Bird’s-eye view of time series forecasting
Determining what must be forecast to achieve your goal
Setting the horizon of the forecast
Developing a forecasting model
1.3 How time series forecasting is different from other regression tasks
Time series sometimes do not have features
1.4 Next steps
2 A naive prediction of the future
2.2 Forecasting the historical mean
Setup for baseline implementations
Implementing the historical mean baseline
2.3 Forecasting last year’s mean
2.4 Predicting using the last known value
2.5 Implementing the naive seasonal forecast
2.6 Next steps
Simulating a random walk process
3.4 Next steps
3.5 Exercises
Simulate and forecast a random walk
Forecast the daily closing price of GOOGL
Forecast the daily closing price of a stock of your choice
Part 2. Forecasting with statistical models
4 Modeling a moving average process
4.1 Defining a moving average process
Identifying the order of a moving average process
4.2 Forecasting a moving average process
4.3 Next steps
4.4 Exercises
Simulate an MA(2) process and make forecasts
Simulate an MA(q) process and make forecasts
5 Modeling an autoregressive process
5.1 Predicting the average weekly foot traffic in a retail store
5.2 Defining the autoregressive process
5.3 Finding the order of a stationary autoregressive process
The partial autocorrelation function (PACF)
5.4 Forecasting an autoregressive process
5.5 Next steps
5.6 Exercises
Simulate an AR(2) process and make forecasts
Simulate an AR(p) process and make forecasts
6 Modeling complex time series
6.1 Forecasting bandwidth usage for data centers
6.2 Examining the autoregressive moving average process
6.3 Identifying a stationary ARMA process
6.4 Devising a general modeling procedure
Understanding the Akaike information criterion (AIC)
Selecting a model using the AIC
Understanding residual analysis
6.5 Applying the general modeling procedure
6.6 Forecasting bandwidth usage
6.7 Next steps
6.8 Exercises
Make predictions on the simulated ARMA(1,1) process
Simulate an ARMA(2,2) process and make forecasts
7 Forecasting non-stationary time series
7.1 Defining the autoregressive integrated moving average model
7.2 Modifying the general modeling procedure to account for non-stationary series
7.3 Forecasting a non-stationary times series
7.4 Next steps
7.5 Exercises
Apply the ARIMA(p,d,q) model on the datasets from chapters 4, 5, and 6
8.1 Examining the SARIMA(p,d,q)(P,D,Q)m model
8.2 Identifying seasonal patterns in a time series
8.3 Forecasting the number of monthly air passengers
Forecasting with an ARIMA(p,d,q) model
Forecasting with a SARIMA(p,d,q)(P,D,Q)m model
Comparing the performance of each forecasting method
8.4 Next steps
8.5 Exercises
Apply the SARIMA(p,d,q)(P,D,Q)m model on the Johnson & Johnson dataset
9 Adding external variables to our model
9.1 Examining the SARIMAX model
Exploring the exogenous variables of the US macroeconomics dataset
9.2 Forecasting the real GDP using the SARIMAX model
9.3 Next steps
9.4 Exercises
Use all exogenous variables in a SARIMAX model to predict the real GDP
10 Forecasting multiple time series
10.2 Designing a modeling procedure for the VAR(p) model
Exploring the Granger causality test
10.3 Forecasting real disposable income and real consumption
10.4 Next steps
10.5 Exercises
Use a VARMA model to predict realdpi and realcons
Use a VARMAX model to predict realdpi and realcons
11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia
11.1 Importing the required libraries and loading the data
11.2 Visualizing the series and its components
11.3 Modeling the data
11.4 Forecasting and evaluating the model’s performance
11.5 Next steps
Part 3. Large-scale forecasting with deep learning
12 Introducing deep learning for time series forecasting
12.1 When to use deep learning for time series forecasting
12.2 Exploring the different types of deep learning models
12.3 Getting ready to apply deep learning for forecasting
Feature engineering and data splitting
12.4 Next steps
12.5 Exercise
13 Data windowing and creating baselines for deep learning
Exploring how deep learning models are trained for time series forecasting
Implementing the DataWindow class
13.3 Next steps
13.4 Exercises
14 Baby steps with deep learning
14.1 Implementing a linear model
Implementing a single-step linear model
Implementing a multi-step linear model
Implementing a multi-output linear model
14.2 Implementing a deep neural network
Implementing a deep neural network as a single-step model
Implementing a deep neural network as a multi-step model
Implementing a deep neural network as a multi-output model
14.3 Next steps
14.4 Exercises
15 Remembering the past with LSTM
15.1 Exploring the recurrent neural network (RNN)
15.2 Examining the LSTM architecture
15.3 Implementing the LSTM architecture
Implementing an LSTM as a single-step model
Implementing an LSTM as a multi-step model
Implementing an LSTM as a multi-output model
15.4 Next steps
15.5 Exercises
16 Filtering a time series with CNN
16.1 Examining the convolutional neural network (CNN)
16.2 Implementing a CNN
Implementing a CNN as a single-step model
Implementing a CNN as a multi-step model
Implementing a CNN as a multi-output model
16.3 Next steps
16.4 Exercises
17 Using predictions to make more predictions
17.1 Examining the ARLSTM architecture
17.2 Building an autoregressive LSTM model
17.3 Next steps
17.4 Exercises
18 Capstone: Forecasting the electric power consumption of a household
18.1 Understanding the capstone project
Objective of this capstone project
18.2 Data wrangling and preprocessing
18.3 Feature engineering
Identifying the seasonal period
Splitting and scaling the data
18.4 Preparing for modeling with deep learning
Utility function to train our models
18.5 Modeling with deep learning
Long short-term memory (LSTM) model
Convolutional neural network (CNN)
18.6 Next steps
Part 4. Automating forecasting at scale
19 Automating time series forecasting with Prophet
19.1 Overview of the automated forecasting libraries
19.2 Exploring Prophet
19.3 Basic forecasting with Prophet
19.4 Exploring Prophet’s advanced functionality
Cross-validation and performance metrics
19.5 Implementing a robust forecasting process with Prophet
Forecasting project: Predicting the popularity of “chocolate” searches on Google
Experiment: Can SARIMA do better?
19.6 Next steps
19.7 Exercises
Forecast the number of air passengers
Forecast the volume of antidiabetic drug prescriptions
Forecast the popularity of a keyword on Google Trends
20 Capstone: Forecasting the monthly average retail price of steak in Canada
20.1 Understanding the capstone project
Objective of the capstone project
20.2 Data preprocessing and visualization
20.4 Optional: Develop a SARIMA model
20.5 Next steps
21.1 Summarizing what you’ve learned
Statistical methods for forecasting
Deep learning methods for forecasting
Automating the forecasting process
21.2 What if forecasting does not work?
21.3 Other applications of time series data
21.5 Keep practicing
Appendix. Installation instructions