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 including classical multiplicative decomposition, STL decomposition, and X-11 seasonal adjustment procedures to isolate trend, seasonal, and irregular components with particular emphasis on Australian labour force dynamics (1978-1995) revealing 38% secular growth dominated by trend components. Key technical achievements include systematic Box-Cox transformation analysis determining optimal variance-stabilizing parameters across diverse datasets—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)—using Guerrero method optimization with clear decision frameworks for transformation necessity, alongside advanced outlier detection utilizing X-11 irregular components to identify structural breaks and anomalous periods in retail data including significant outliers during the early 2000s economic expansion while quantifying outlier effects on seasonal adjustment procedures and demonstrating superior detection capabilities compared to classical methods. The analysis employs a comparative analytical framework systematically evaluating transformation effectiveness through before/after visualizations and statistical validation, applying consistent protocols across heterogeneous datasets to demonstrate scalable methodological approaches suitable for production-level forecasting environments that directly support strategic decision-making in economic forecasting, retail planning, and resource allocation optimization. This demonstrated capability to parse complex temporal signals into interpretable components enables evidence-based policy recommendations and risk assessment protocols essential for senior analytical roles in data-driven organizations, showcasing proficiency in R/fpp3, advanced time series modeling, statistical transformation theory, and macroeconomic data analysis with clear business applications for companies requiring sophisticated analytical infrastructure for temporal pattern recognition and forecasting.

Library WiFi and Energy Data Analysis

Tugas Matkul EDA 1

Kelompok 13

TASK 1

Analysis of Library Energy Consumption Based on WiFi Occupancy This project analyzes the relationship between a library building's occupancy and its total energy consumption. Using the number of WiFi device connections as a proxy for occupancy, data from both sources were combined and analyzed. The results show a strong positive correlation: the more people in the building, the higher the energy consumption. The analysis also successfully identified clear daily usage patterns, with peak activity occurring during weekday afternoons, which is significantly different from the much quieter pattern on weekends.