Oreilly – Time Series Forecasting in Python, Video Edition 2022-11

Description

Time Series Forecasting in Python Video Edition. This course is an audiobook-like experience. In this course, the narrator reads the book while the book content, charts, Python code, and explanatory text are displayed on the screen. The goal of this course is to teach you how to build forecasting models based on temporal patterns in your data. In this course, you will learn how to derive accurate and understandable forecasts from time-based data such as logs, customer analytics, and other event streams. By using statistical methods and deep learning for time series forecasting, you will be able to build powerful models.

What you will learn:

• Identifying the time series forecasting problem and building high-performance forecasting models
• Building univariate forecasting models that take into account seasonal effects and external variables
• Building multivariate forecasting models to forecast multiple time series simultaneously
• Using deep learning to predict time series in large datasets
• Automate the forecasting process

This course is suitable for people who:

• Familiar with the Python programming language and the TensorFlow library
• Work as a data scientist and seek to learn time series forecasting methods

Time Series Forecasting in Python Video Edition specification

• Publisher: Oreilly
• Instructor:   Marco Peixeiro
• Training level: Beginner to advanced
• Training duration: 11 hours and 4 minutes

Course headings

• Part 1. Time waits for no one
• Chapter 1. Understanding time series forecasting
• Chapter 1. Bird’s-eye view of time series forecasting
• Chapter 1. How time series forecasting is different from other regression tasks
• Chapter 2. A naive prediction of the future
• Chapter 2. Implementing the historical mean baseline
• Chapter 2. Forecasting last year’s mean
• Chapter 3. Going on a random walk
• Chapter 3. Identifying a random walk
• Chapter 3. Testing for stationarity
• Chapter 3. The autocorrelation function
• Chapter 3. Forecasting a random walk
• Chapter 3. Next steps
• Part 2. Forecasting with statistical models
• Chapter 4. Modeling a moving average process
• Chapter 4. Identifying the order of a moving average process
• Chapter 4. Forecasting a moving average process Part 1
• Chapter 4. Forecasting a moving average process Part 2
• Chapter 4. Next steps
• Chapter 5. Modeling an autoregressive process
• Chapter 5. Finding the order of a stationary autoregressive process
• Chapter 5. Forecasting an autoregressive process
• Chapter 6. Modeling complex time series
• Chapter 6. Examining the autoregressive moving average process
• Chapter 6. Identifying a stationary ARMA process
• Chapter 6. Devising a general modeling procedure
• Chapter 6. Selecting a model using the AIC
• Chapter 6. Understanding residual analysis
• Chapter 6. Applying the general modeling procedure
• Chapter 6. Forecasting bandwidth usage
• Chapter 6. Exercises
• Chapter 7. Forecasting non-stationary time series
• Chapter 7. Forecasting a non-stationary time series Part 1
• Chapter 7. Forecasting a non-stationary time series Part 2
• Chapter 8. Accounting for seasonality
• Chapter 8. Forecasting the number of monthly air passengers
• Chapter 8. Forecasting with a SARIMA(p,d,q)(P,D,Q)m model
• Chapter 9. Adding external variables to our model
• Chapter 9. Exploring the exogenous variables of the US macroeconomics dataset
• Chapter 9. Forecasting the real GDP using the SARIMAX model
• Chapter 10. Forecasting multiple time series
• Chapter 10. Designing a modeling procedure for the VAR(p) model
• Chapter 10. Forecasting real disposable income and real consumption
• Chapter 10. Next steps
• Chapter 11. Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia
• Chapter 11. Performing model selection
• Part 3. Large-scale forecasting with deep learning
• Chapter 12. Introducing deep learning for time series forecasting
• Chapter 12. Getting ready to apply deep learning for forecasting
• Chapter 12. Feature engineering and data splitting
• Chapter 13. Data windowing and creating baselines for deep learning
• Chapter 13. Implementing the DataWindow class
• Chapter 13. Multi-step baseline models
• Chapter 14. Baby steps with deep learning
• Chapter 14. Implementing a multi-output linear model
• Chapter 14. Implementing a deep neural network as a multi-step model
• Chapter 15. Remembering the past with LSTM
• Chapter 15. Examining the LSTM architecture
• Chapter 15. Implementing the LSTM architecture
• Chapter 15. Implementing an LSTM as a multi-output model
• Chapter 16. Filtering a time series with CNN
• Chapter 16. Implementing a CNN
• Chapter 16. Implementing a CNN as a multi-step model
• Chapter 17. Using predictions to make more predictions
• Chapter 17. Building an autoregressive LSTM model
• Chapter 18. Capstone: Forecasting the electric power consumption of a household
• Chapter 18. Data wrangling and preprocessing
• Chapter 18. Feature engineering
• Chapter 18. Utility function to train our models
• Chapter 18. Long short-term memory (LSTM) model
• Part 4. Automating forecasting at scale
• Chapter 19. Automating time series forecasting with Prophet
• Chapter 19. Exploring Prophet
• Chapter 19. Basic forecasting with Prophet
• Chapter 19. Exploring Prophet’s advanced functionality
• Chapter 19. Hyperparameter tuning
• Chapter 19. Forecasting project: Predicting the popularity of “chocolate” searches on Google
• Chapter 19. Experiment: Can SARIMA do better?
• Chapter 20. Capstone: Forecasting the monthly average retail price of steak in Canada
• Chapter 20. Modeling with Prophet
• Chapter 20. Optional: Develop a SARIMA model
• Chapter 21. Going above and beyond
• Chapter 21. Deep learning methods for forecasting
• Chapter 21. Other applications of time series data

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