Visualizing Trends and Seasonality

Real-world time series often contain trends (long-term direction), seasonality (repeating patterns), and noise. Effective visualizations separate these components.

Moving Averages for Trends

Seasonal Decomposition

Year-over-Year Comparison

Seasonal Heatmap

Trend Lines and Forecasting Visualization

Cycle Detection with Autocorrelation

Practice: Seasonal Sales Analysis

Key Takeaways

• Moving averages smooth out noise to reveal trends
• Larger window sizes create smoother but more lagged trends
• Decomposition separates trend, seasonality, and residuals
• Year-over-year comparisons highlight growth and seasonal patterns
• Seasonal heatmaps show both patterns across years
• Autocorrelation reveals hidden cycles in the data
• Forecast visualizations should include uncertainty bands

In the next module, we'll preview interactive plotting capabilities.