Unveiling Patterns Using Time Series Visualization

The article discusses the importance of time series visualization in understanding complex datasets, particularly in identifying trends, cycles, seasonality, and anomalies that may remain obscured in tabulated figures. Various techniques for time series visualization are presented, including line charts, area charts, stacked area charts, heat maps, histograms, box plots, correlogram/autocorrelation plots, and more. The effectiveness of these methods is demonstrated through a mock AirPassengers dataset, which reveals an upward trend in the number of airline passengers over time, seasonal patterns showing the busiest and least busy months for international air travel, and year-over-year growth rates indicating the industry's expansion pace.