Advanced Statistical Models in Forecasting

Advanced Statistical Models in Forecasting offers a comprehensive exploration of sophisticated statistical methodologies tailored for demand forecasting within the retail, trade, and logistics sectors. This course emphasizes hands-on, project-based learning, where participants will engage in real-world applications of statistical models to predict demand accurately. By integrating theory with practical exercises, learners will develop the skills necessary to analyze data effectively, interpret results, and make informed decisions that enhance operational efficiency.
Participants will delve into a variety of advanced statistical techniques, including time series analysis, regression models, and machine learning approaches. The course encourages collaboration and knowledge sharing, culminating in a final project that allows learners to apply their acquired skills to a specific forecasting challenge. Additionally, students are invited to publish their findings in Cademix Magazine, contributing to the broader professional community.
Introduction to demand forecasting and its significance in retail and logistics
Overview of statistical models used in forecasting
Time series analysis: decomposition, trend analysis, and seasonal variations
Regression analysis for demand prediction
Machine learning algorithms in forecasting: an introduction
Evaluating forecasting accuracy: metrics and methodologies
Advanced techniques: ARIMA and Exponential Smoothing
Scenario planning and its impact on demand forecasting
Data visualization tools for presenting forecasting results
Final project: Developing a comprehensive forecasting model for a selected case study

Equip participants with advanced skills in statistical modeling for effective demand forecasting.

Graduates, job seekers, business professionals, and optionally researchers or consultants.

Basic knowledge of statistics and familiarity with data analysis tools (e.g., Excel, R, or Python).