This comprehensive course provides specialized training in time series analysis and forecasting methods for business and economic data. Participants will learn to analyze temporal patterns, build accurate forecasting models, and make data-driven predictions for future trends. The curriculum covers decomposition techniques, ARIMA modeling, exponential smoothing, and modern machine learning approaches for time series. Through practical applications, learners will develop the skills to create reliable forecasts that support inventory management, financial planning, and strategic decision-making.
Time Series Analysis and Forecasting
Data Analytics and Business Intelligence
October 25, 2025
Introduction
Objectives
Key learning objectives include:
- Understand time series components and patterns
- Implement decomposition techniques for trend and seasonality
- Build and validate ARIMA models
- Apply exponential smoothing methods
- Use machine learning for time series forecasting
- Evaluate forecast accuracy and uncertainty
- Handle non-stationary and seasonal data
- Develop forecasting systems for business applications
Target Audience
- Business forecasters and planners
- Financial analysts and economists
- Supply chain and operations analysts
- Data scientists and statisticians
- Marketing and sales analysts
- Risk management professionals
- Academic researchers
Methodology
The course uses real-time series datasets from business, economics, and industry to provide practical forecasting experience. Participants work through scenarios involving sales forecasting, demand planning, and financial prediction. Case studies demonstrate time series applications across sectors, while group activities focus on collaborative model development. Individual exercises build technical skills, and mini-case studies present specific forecasting challenges. Syndicate discussions explore model selection and business implications of forecasts.
Personal Impact
- Enhanced ability to analyze temporal patterns
- Improved skills in building accurate forecasting models
- Stronger understanding of time series components
- Increased confidence in making data-driven predictions
- Better evaluation of forecast uncertainty
- Developed ability to implement forecasting systems
Organizational Impact
- More accurate business forecasts and planning
- Improved inventory management and optimization
- Better resource allocation based on predictions
- Enhanced financial planning and budgeting
- Reduced costs through better demand forecasting
- Increased responsiveness to market changes
Course Outline
Unit 1: Time Series Fundamentals
Basic Concepts- Time series components (trend, seasonality, cycle)
- Stationarity and autocorrelation
- Time series visualization techniques
- Data preparation for time series analysis
Unit 2: Decomposition Methods
Component Analysis- Additive and multiplicative decomposition
- Moving averages and smoothing
- Seasonal adjustment methods
- Anomaly detection in time series
Unit 3: ARIMA Modeling
Box-Jenkins Methodology- Autoregressive (AR) models
- Moving average (MA) models
- ARIMA model identification and estimation
- Seasonal ARIMA (SARIMA) models
- Residual analysis and white noise testing
- Model selection criteria
- Parameter significance testing
- Forecast interval construction
Unit 4: Exponential Smoothing
Smoothing Techniques- Simple exponential smoothing
- Holt-Winters seasonal smoothing
- Error-trend-seasonal (ETS) models
- Smoothing parameter optimization
Unit 5: Advanced Forecasting Methods
Machine Learning Approaches- Regression trees for time series
- Neural networks and deep learning
- Ensemble methods for forecasting
- Multivariate time series models
Unit 6: Business Applications
Industry Implementation- Demand forecasting in retail
- Financial market prediction
- Inventory and supply chain planning
- Energy load forecasting
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