Time Series Modeling and Forecasting (SAS)
- Analyzed time-dependent data to identify seasonality and trend
- Used autocorrelation to determine model structure
- Built and tested forecasting models (exponential trend with seasonal dummies, AR errors, Winter’s smoothing, Box–Jenkins)
- Applied log transformation and differencing to stabilize data
- Evaluated models using white noise diagnostics and RMSE
- Compared short- and long-term forecasts to select a final model
- Summarized model results and interpretation in a written report
Data, Financial, and Database Modeling
- Built Excel-based models to analyze costs, cash flows, and performance
- Designed structured databases for data validation and retrieval
- Performed data reconciliation and summary analysis
Python Programming and Quantitative Computing
- Used Python to solve mathematical and algorithmic problems
- Implemented basic numerical and logic-based solutions