Applied Forecasting in Retail focuses on equipping participants with advanced methodologies for analyzing and predicting retail trends through time series analysis. This course delves into statistical techniques and data-driven approaches that empower professionals to make informed decisions based on historical data patterns. Participants will engage in hands-on projects that simulate real-world retail scenarios, fostering an environment where theoretical knowledge is directly applied to practical challenges.
The curriculum is structured to enhance analytical skills through interactive learning experiences. Participants will explore various forecasting models, gain proficiency in software tools, and collaborate on a final project that showcases their ability to generate actionable insights for retail forecasting. By the end of the course, learners will possess the skills necessary to interpret data effectively, enhancing their value in the job market and contributing to their organizations’ success.
Introduction to Time Series Analysis in Retail
Data Collection and Preparation for Forecasting
Exploratory Data Analysis Techniques
Seasonal Decomposition of Time Series
Moving Averages and Exponential Smoothing
Autoregressive Integrated Moving Average (ARIMA) Models
Advanced Forecasting Techniques (e.g., SARIMA, ETS)
Evaluating Forecast Accuracy and Model Selection
Implementing Forecasting in Retail Decision-Making
Final Project: Developing a Comprehensive Forecasting Model for a Retail Scenario