This course dives into advanced time series modeling techniques, focusing on SARIMA (Seasonal AutoRegressive Integrated Moving Average) and other methods for handling complex seasonal data. Participants will learn how to model and forecast time series with seasonal patterns and evaluate their performance. Practical examples and hands-on labs with real-world datasets will ensure attendees develop expertise in applying these advanced techniques.
Session 1: Introduction to SARIMA
Session 2: Data Preparation for Seasonal Models
Session 1: Building and Tuning SARIMA Models
Session 2: Real-World Applications and Case Studies
We are open to customizing this program to align with your specific learning objectives. If your team has particular goals or areas they wish to focus on, we would be happy to tailor the course outline to meet those needs and ensure the program supports the achievement of your desired outcomes.
This course provides an introduction to time series analysis, focusing on its concepts, components, and fundamental applications.
This course introduces participants to the basics of time series forecasting, focusing on techniques like moving averages and exponential smoothing.
This course focuses on identifying and modeling seasonal patterns and trends in time series data.
This practical workshop teaches participants how to use Excel for analyzing and forecasting time series data.
This course provides in-depth training on using ARIMA (AutoRegressive Integrated Moving Average) models for accurate time series forecasting.
This course focuses on techniques to effectively visualize time series data, enabling participants to identify trends, seasonality, and anomalies.
This course provides hands-on training in time series analysis using Python libraries such as Pandas, Matplotlib, and Statsmodels.