Project work for Time Series Analysis. Includes exploratory analysis, ARIMA modeling, diagnostics, forecasting, and evaluation using R. Covers trend/seasonality modeling, stationarity checks, ACF/PACF analysis, model selection, and forecast accuracy assessment.

A comparative audit of 15+ time series architectures (ARIMA-GARCH, XGBoost, Transformers) across four temporal domains. Focuses on predictive robustness, residual adequacy (Ljung-Box, ARCH-LM), and statistical significance testing.

Regression analysis of climate variables and global crop yields using R

STL decomposition notebook with parameter analysis, diagnostics, seasonal strength metrics, forecasting, and alternative methods. Complete time series decomposition workflow with visualizations and statistical tests.

PIT-based quantile and randomized quantile residual (RQR) diagnostics for Stata models.

Built and validated a linear regression model to predict annual customer spend using session and membership features; achieved R² = 0.981 and RMSE ≈ $10 on hold-out test data.

Forecasting-first thesis repository for spatio-temporal wind power forecasting on the DaKS/Kassel synthetic wind power dataset, with benchmark-safe diagnostics and graph-aware evaluation.