TP 7_Parte2

Forecasting Australian Retail Time Series: A Comprehensive Analysis Dataset Overview This analysis examines Australian retail turnover data from the `aus_retail` dataset, focusing on time series forecasting methodologies and residual diagnostics. The study encompasses multiple retail sectors and employs various forecasting techniques to evaluate predictive performance. Key Analytical Components Time Series Characteristics: The dataset reveals diverse patterns across different retail categories, with seasonal variations, trending behaviors, and structural changes evident throughout the observation period from the 1980s through 2010s. Forecasting Methods Applied: - Seasonal Naive (SNAIVE) for capturing repetitive seasonal patterns - Random Walk with Drift for trending data - Naive methods for baseline comparisons Model Validation Framework: Comprehensive residual analysis using three-panel diagnostic plots examining temporal patterns, autocorrelation functions (ACF), and distributional properties to assess white noise assumptions. Notable Findings Residual Analysis: The study revealed that simple forecasting methods often fail to capture complex underlying structures in retail data. Residuals frequently exhibited non-random patterns, autocorrelation, and heteroscedasticity, indicating opportunities for more sophisticated modeling approaches. Structural Changes: Evidence of significant structural breaks and unusual events (particularly around 1995-1997) suggests external economic factors substantially impact retail performance beyond seasonal patterns. Training Data Sensitivity: Forecast accuracy demonstrates notable sensitivity to training period selection, with implications for practical forecasting applications in retail planning. Technical Implementation The analysis leverages the `fpp3` package ecosystem in R, employing modern tidyverse principles for data manipulation and the `tsibble` framework for time series operations. Cross-validation techniques separate training and test periods to ensure robust accuracy assessment. This comprehensive approach provides valuable insights into Australian retail dynamics while demonstrating practical applications of time series forecasting methodologies in economic analysis.

Html file homework for week 2 of BAIS 329.