Advanced Forecasting Methods

using Forgetting variables

3-Days Workshop

April 2025

Basic Course Offering (Virtual, Group Format):

Standard fees: 380 Euros

Discounted fees (Affiliates, former trainees, and participants from M&S Research Hub list of developing countries): 197 Euros

Early-bird discount (1 Month in advance): 20% discount

Contact for Onsite/Private/Corporate Trainng

Workshop Description

Workshop Content

This comprehensive 6-hour workshop focuses on advanced forecasting methods that leverage the innovative concept of "forgetting variables" to significantly improve predictive accuracy.

By the end of this workshop, participants will have gained a deep understanding of advanced forecasting methods using forgetting variables, equipping them with the skills and knowledge to enhance the accuracy and adaptability of their forecasting models in dynamic business environments.

Introduction to Bayesian Statistics:

- Bayesian Analysis in the linear regression model
- Bayesian Computation: Gibbs sampler and the Metropolis-Hasting algorithm

Introduction to Forgetting Variables:

- Understanding the concept of forgetting variables in forecasting
- Exploring scenarios where traditional models may fall short and the need for adaptability arises.

Dynamic Time Series Modeling:

- Techniques for capturing and modeling time series data that exhibit changing patterns over time.
- Case studies illustrate the limitations of static models in dynamic environments.

Real-world Applications:

- Industry-specific case studies showcasing the successful application of forgetting variables in forecasting.
- Discussion on challenges and solutions encountered in various domains.

Model Evaluation and Validation:

- Techniques for assessing the performance of models incorporating forgetting variables.
- Strategies for validating the adaptability and robustness of forecasting models over time.
- Practical guidance on implementing forgetting variables in existing forecasting frameworks.
- Considerations for selecting the appropriate algorithms based on data characteristics and forecasting goals.

Q&A and Discussions:

- Interactive sessions for participants to ask questions and engage in discussions with experienced practitioners.
- Sharing insights and best practices among participants.

Meet The

Moderator

William Asiedu is Economic Researcher and Program Officer with the National Graduate Institute for Policy Studies (GRIPS’) Social Accelerator Program in Tokyo, Japan. William is also a Data Analysis consultant at the Center for Data Science (GRIPS). He is a Ph.D. candidate in Policy Analysis using econometrics tools to understand forecasting models and how the open economy operates.

Benefits

Workshop material, datasets, and software codes will be freely available to participants.
Earn MSR certified certificate and enhance your career and research skills.
Learn one of the emerging and widely used approaches for forecasting and prediction
A free trial version of the used software packages will be provided for participants

Target Participants

Consultants & Advisors

Professionals providing consultancy or advisory services in the areas of data science, analytics, and forecasting.

Marketing & Sales Analysts

Professionals in marketing and sales analytics looking to enhance their predictive modeling skills for demand forecasting and campaign planning.

Researchers

Researchers and educators in the fields of data science, statistics, and business who want to stay informed about the latest advancements in forecasting methods.

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