Unveiling Patterns Using Time Series Visualization

This post details techniques for visualizing time series data to uncover patterns like trends and seasonality. It demonstrates using line charts, area charts, heat maps, histograms, and box plots. It also shows how to decompose a time series into trend, seasonal, and residual components using `statsmodels.tsa.seasonal.seasonal_decompose` and how to visualize the average seasonal component by month. The post uses a mock AirPassengers dataset and provides Python code examples for data generation and visualization.