Time Series Forecasts ★★★ Expert Level
Reliable forecasts can be used to gain a competitive advantage for business steering and give your company an edge in the market. This course will teach you the skills required to create a unique data-driven forecast system for your business.
Topics Supervised Machine Learning
Badge image
2 days
Recommended Level
Upcoming courses

Time Series Forecasts

About the course

Companies are ruled by their future expectations, basing their investments and success on forecasts, so correct forecasting is key. However, there are so many different forecast models that it is hard to know which one to use when. Every business needs a unique forecasting perspective depending on the granularity and variability of the data, seasonality, trends, and forecast horizon. Our forecast training provides you with a lens to critically identify where to implement and improve forecasting in your organization by breaking down the steps and best practices used by our expert trainers. This course provides basic and advanced modeling tools to develop your own data-driven forecast system using real-life cases. As well as perform these techniques practically and make forecasts in Python.  

Why this is for you

Application forecasting is key for all businesses to find out their trajectory in the next periods and be equipped to reach their goals and ambitions in the future. But this is easier said than done, as using the wrong method or a biased forecast could result in using incorrect output for strategic steering and actually leave you worse off. We will teach you to understand the best methods, when and how to use them, so you can create a significantly better forecast.  

For whom

This course is designed for Data Scientists wanting to improve their forecasting techniques and get data-driven results. Please note it is recommended you have completed our Regression Models (3203) badge before starting this training.  

What you’ll learn

  1. Introduction and overview of forecasting
  2. Basic forecasting techniques Holt-Winters
  3. Advanced forecasting techniques TBATS and SARIMAX
  4. To explore time series and make a data decomposition
  5. To apply K-fold cross-validation techniques for forecasting
  6. How to perform advanced forecasting techniques on real data
  7. How to complete deviation management on forecasting case
Learning Goals
  • Overview of forecasting – Understand and explain the importance and essential principles of forecasting
  • Basic forecasting techniques – Perform basic forecasting techniques and make a data decomposition
  • Forecasting metrics and validation techniques – Complete k-fold cross-validation using correct metrics
  • Advanced forecasting techniques – Perform advanced forecasting techniques and understanding what techniques to use depending on the situation
  • Deviation management as part of model management – Make a deviation analysis based on a comparison of the forecast to the actuals
Theory and practical use All trainings in the GAIn portfolio combine high-quality standardized training material with theory sessions from experts and hands-on experience where you directly apply the material to real-life cases. Each training is developed by top of the field practitioners which means they are full of industry examples along with practical challenges and know-how, fueling the interactive discussions during training. We believe this multi-level approach creates the ideal learning environment for participants to thrive.

AI quick scan

Test how your company is performing on developing and implementing AI solutions and platform capabilities:


1.My organisation communicates a clear end-state vision on how AI can transform our business
2.AI initiatives are aligned with the strategic goals of my organisation
3.My organisation has a roadmap that focuses data analytics efforts on large scale business opportunities
4.My organisation succesfully builds high potential proof of concepts with AI
5.AI use cases are developed end-to-end from data to algorithm to an application, such that users can interact with the algorithm
6.AI use cases are automated and operationalized in a production environment
7.AI use cases are fully adopted by the business, without unintended retreat to own judgement
8.AI use cases change the business process fundamentally if adopted
9.AI use case MVP's are continuously being improved, leaving MVP state
10.My organisation succesfully scales impact of AI use cases
11.There is an infrastructure in place for users to approach (a selection of) data
12.There is a possibility to develop models, for example in sandbox like environments
13.Algorithms are operationalized and automatically predict on new data
14.Your organisation has deployed one ore more experiments with modern, for example cloud-based, infrastructure
15.AI platform standards are set and managed
16.Clarity exists on target infrastructure for new solutions
17.Existence of legacy is accepted as a given, but migrations and dependencies are managed with a impact driven mindset
18.AI solutions are in production on scalable modern infrastructure
19.There are standardized operational processes to develop, deploy and maintain models (model management) in modern infrastructure.
20.Users enjoy flexibility to access any data and use it to build solutions with diverse requirements, while still maintaining certain standards managed centrally