Time series data — observations recorded at successive points in time — is arguably the most common type of data in business, science, and government. Visualizing it effectively requires understanding the unique properties of temporal data: seasonality, trends, irregular sampling intervals, and the distinction between real-time and historical perspectives.
The line chart remains the foundation of time series visualization. Its continuous nature matches our mental model of time flowing smoothly, and its slope communicates rate of change intuitively. When plotting multiple time series on the same chart, use distinct colors and direct labeling rather than a separate legend to reduce the cognitive effort of matching colors to series names. For datasets with more than five or six series, consider small multiples — a grid of mini line charts, each showing one series — rather than cramming everything into one unreadable rainbow.
Area charts add semantic weight by filling the space beneath a line, making them ideal for showing cumulative totals or emphasizing magnitude. Stacked area charts reveal how components contribute to a whole over time, though they share the same readability limitations as stacked bar charts once you exceed three or four layers. For financial time series, candlestick and OHLC charts remain the standard, compactly encoding open, high, low, and close values for each period.
Seasonal decomposition is one of the most powerful analytical techniques for time series. By splitting a series into trend, seasonal, and residual components — and visualizing each separately — you can spot patterns that are invisible in the raw data. A retail sales series might reveal a steady upward trend, a strong December seasonal peak, and residual spikes during promotional campaigns, each requiring different business responses.