Five-Project Series

Time Series Forecasting with Bayesian Modeling you own this product

intermediate Python • basic TensorFlow • intermediate time series forecasting • intermediate time series analysis
skills learned
data manipulation with pandas • time series forecasting with pmdarima and Prophet • PyMC3 • TensorFlow probability
Michael Grogan
5 weeks · 4-6 hours per week average · ADVANCED

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Bayesian-based probability and time series methods allow data scientists to adapt their models to uncertainty and better predict outcomes. In this series of liveProjects, you’ll take on the role of a data scientist making customer predictions for hotels and airlines. You’ll use ARIMA, Bayesian dynamic linear modeling, PyMC3 and TensorFlow Probability to model hotel booking cancelations, and implement a Prophet model with uncertainty analysis to forecast air passenger numbers. Each project in the series is focused on a different time series forecasting model, allowing you to compare model performance and choose the skills most relevant to your career development.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

here's what's included

Project 1 Data Manipulation and ARIMA Modeling with Pyramid
In this liveProject, you’ll investigate seasonality in hotel cancellations by building an ARIMA model that can predict cancellations on a weekly basis. You’ll learn how to manipulate a dataset with pandas in order to form a weekly time series, before going on to make your first predictions.
Project 2 Prophet Model Incorporated with Bayesian Analysis
In this liveProject, you’ll build a Prophet model that can forecast airline passenger numbers using data from the DataSF portal. The hotel you work for believes that analyzing the travel trends of US customers will help them forecast potential travel to Europe, and bookings in the hotel. You’ll enhance your model with "changepoints" that mark a significant change in trends, and make adjustments so your model can account for uncertainty in the trend and seasonality components.
Project 3 Bayesian Dynamic Linear Modeling
In this liveProject, you’ll build a Bayesian dynamic linear model that can take account of sudden state space changes and rapidly react to dramatic trend changes. These trend changes could take many forms—from heightened demand during a major sporting event, to a global pandemic that causes cancellations to skyrocket. You’ll use the PyDLM library to generate forecasts that can dynamically adapt to the unforeseen, and quickly shift to making accurate predictions for a new reality.
Project 4 Bayesian Statistical Methods with PyMC3
In this liveProject, you’ll use PyMC3 to generate a posterior distribution of hotel cancellations. This will allow you to build a predictive model that can update its predicted probabilities by incorporating new information. You’ll master methods for modelling mean and standard deviations based on the selected prior values, generating distributions to determine if data follows an AR(1) process, modeling of stochastic volatility and generalized linear modelling with PyMC3.
Project 5 Time Series Modeling with TensorFlow Probability
In this liveProject, you’ll combine the power of deep learning with probabilistic modeling. You’ll build a structural time series model that can develop probabilistic forecasts of hotel cancellations, and use this model to identify anomalies across your cancellation data. You’ll perform a similar analysis of an air passenger dataset, and then use Bayesian Switchpoint analysis to determine the approximate time interval in which searches for the term “vacation” declined.

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project author

Michael Grogan
Michael Grogan is a data scientist with expertise in TensorFlow and time series analysis. His educational background is a Master's degree in Economics from University College Cork, Ireland. As such, much of his work has been in the domain of business intelligence, i.e. using machine learning technologies to develop solutions to a wide range of business problems. He has implemented time series solutions for organizations across a range of industries through the implementation of statistical analysis as well as more advanced machine learning methodologies. In addition, he has delivered numerous seminars and training courses in the areas of data science and machine learning, including for Manning and O'Reilly Media.


This liveProject is for data analysts with a basic understanding of time series methods and data manipulation tools in Python including pandas. To begin this liveProject, you will need to be familiar with the following:

  • Intermediate knowledge of Python, particularly the pandas, NumPy, and sklearn libraries

    • Intermediate time series methodologies


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.