Advanced Time Series Analysis and Decomposition Techniques This comprehensive time series analysis demonstrates advanced statistical modeling capabilities across multiple economic datasets, employing sophisticated decomposition methodologies and transformation techniques. The assignment showcases proficiency in handling complex temporal data structures, applying appropriate statistical transformations, and extracting meaningful insights from macroeconomic indicators. Key Technical Achievements Box-Cox Transformation Analysis: Systematically determined optimal variance-stabilizing transformations across diverse datasets including Canadian gas production (λ = 0.577), Australian retail series (λ = 0.371), tobacco production (λ = 0.926), airline passengers (λ = 2.0), and pedestrian traffic (λ = 0.273). Applied Guerrero method optimization to identify appropriate transformation parameters and demonstrated clear decision frameworks for transformation necessity. Advanced Decomposition Methodologies: Implemented multiple decomposition techniques including classical multiplicative decomposition, STL decomposition, and X-11 seasonal adjustment procedures. Successfully isolated trend, seasonal, and irregular components across various time series, with particular emphasis on Australian labour force dynamics (1978-1995) revealing 38% secular growth dominated by trend components. Outlier Detection and Impact Analysis: Utilized X-11 irregular components to identify structural breaks and anomalous periods in retail data, including significant outliers during the early 2000s economic expansion. Quantified outlier effects on seasonal adjustment procedures and demonstrated superior outlier detection capabilities compared to classical methods. Comparative Analytical Framework: Systematically evaluated transformation effectiveness through before/after visualizations and statistical validation. Applied consistent analytical protocols across heterogeneous datasets, demonstrating scalable methodological approaches suitable for production-level forecasting environments. Strategic Business Applications This analysis framework directly supports strategic decision-making in economic forecasting, retail planning, and resource allocation optimization. The demonstrated capability to parse complex signals into interpretable components enables evidence-based policy recommendations and risk assessment protocols essential for senior analytical roles in data-driven organizations. Technical Stack: R/fpp3, advanced time series modeling, statistical transformation theory, macroeconomic data analysis

In this lab I explore flights, specifically a random sample of domestic flights that departed from the three major New York City airports in 2013. I generated simple graphical and numerical summaries of data on these flights and explore delay times.

This data report presents an analysis of a marketing dataset from a Portuguese banking institution's direct marketing campaigns. The dataset focuses on phone-based marketing efforts aimed at promoting term deposits to clients. The primary objective is to develop a predictive classification model that determines whether a client will subscribe to a term deposit (binary outcome: 'yes' or 'no'). The campaigns often required multiple contacts with the same client to achieve successful conversions, making this a complex customer behavior prediction problem."