In examining the dataset for our project, which includes data on airport statistics, hotel occupancy, employment rates, and housing market indicators, I plan to conduct time series analysis to uncover temporal patterns and insights. To kick things off, I’ll start by visually exploring time series plots for each variable, looking out for any noticeable trends over time. Employing techniques like seasonal-trend decomposition using LOESS (STL), I’ll break down the time series into components like trend, seasonality, and residual to gain a deeper understanding of the data.
Correlation analysis will be crucial to identifying relationships between different variables, helping me comprehend how changes in one variable may align with changes in others. Moving on, forecasting models such as AutoRegressive Integrated Moving Average (ARIMA) or Seasonal ARIMA (SARIMA) will be applied to predict future values, particularly for variables like monthly passenger numbers and hotel occupancy rates.
I’ll also be on the lookout for anomalies or outliers using statistical methods to provide insights into exceptional events within the dataset. Exploring causal relationships between variables is another key aspect; for instance, I’ll investigate whether changes in employment rates correlate with shifts in hotel occupancy or other economic indicators.
To effectively communicate my findings, visualizations like time series plots and stacked area charts will come in handy. Additionally, I’ll apply statistical testing to assess the significance of observed trends or differences. By following these steps systematically, I aim to uncover valuable insights into the temporal dynamics of the dataset, enhancing our understanding of patterns and enabling us to make informed predictions for future trends in the context of our project.