Further Reading: Time Series Visualization
Tier 1: Essential Reading
Hyndman, Rob J., and George Athanasopoulos. Forecasting: Principles and Practice. 3rd ed. OTexts, 2021. The canonical modern book on time series analysis and forecasting. Covers decomposition, rolling statistics, ARIMA, exponential smoothing, and visualization throughout. Freely available at otexts.com/fpp3. Essential for anyone working with time series data.
matplotlib.dates documentation. matplotlib.org/stable/api/dates_api.html The official reference for matplotlib's datetime handling: locators, formatters, and utility functions. Essential for any non-trivial time series visualization in matplotlib.
**Hawkins, Ed. "Climate Stripes." showyourstripes.info The website where Ed Hawkins's warming stripes are distributed. Includes versions for many countries, cities, and variables. Read alongside Case Study 1.
Tier 2: Recommended Specialized Sources
Cleveland, William S. Visualizing Data. Hobart Press, 1993. Cleveland's book on visualization, including his classic work on banking to 45 degrees. The chapter on time series addresses aspect ratio, smoothing, and trend perception with empirical evidence.
Tufte, Edward R. Beautiful Evidence. Graphics Press, 2006. Chapter 2 introduces sparklines with many examples. Tufte argues for sparklines as inline, word-sized time series charts and provides design principles.
Shneiderman, Ben, and Catherine Plaisant. "Interactive Visualization for the 21st Century." Elsevier, 2015. A survey paper covering interactive time series exploration, including range sliders, brush-and-link, and dynamic queries. Useful for understanding the design space of interactive temporal visualizations.
statsmodels documentation: Time Series Analysis. statsmodels.org/stable/tsa.html
The official reference for statsmodels' time series tools, including seasonal_decompose, STL, ARIMA, and exponential smoothing. Use alongside the Hyndman book.
Few, Stephen. Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press, 2009. Chapter 8 covers time series visualization with emphasis on business dashboards. Practical guidance on axis design, smoothing, and comparison.
Schutt, Rachel, and Cathy O'Neil. Doing Data Science. O'Reilly Media, 2013. Chapter on time series is a practical introduction for data scientists. Less rigorous than Hyndman but more directly applicable to data-science workflows.
Tier 3: Tools and Online Resources
| Resource | URL / Source | Description |
|---|---|---|
| pandas datetime documentation | pandas.pydata.org/docs/user_guide/timeseries.html | Comprehensive guide to pandas's datetime features: parsing, resampling, rolling, time zones. |
| calplot | github.com/tomkwok/calplot | Python library for calendar heatmaps. A few lines of code produce a full calendar visualization. |
| statsmodels STL | statsmodels.org/stable/generated/statsmodels.tsa.seasonal.STL.html | STL decomposition reference. |
| Plotly time series examples | plotly.com/python/time-series/ | Official Plotly time series documentation with range sliders, rangeselectors, and OHLC charts. |
| Prophet | facebook.github.io/prophet/ | Facebook's time series forecasting library, with built-in visualization for forecasts, components, and changepoints. |
| FRED (St. Louis Fed) | fred.stlouisfed.org | Economic time series data with excellent charts. Useful for practicing long-term financial visualization. |
| NOAA climate data | ncdc.noaa.gov | Climate time series data for the progressive project. Includes temperature, CO2, precipitation, and more. |
| Bloomberg terminal charts | (commercial) | The gold standard for financial time series (see Ch 21 Case Study 1). |
| Tradingview | tradingview.com | Free web-based financial charting with extensive customization. Worth exploring to see mature financial chart conventions. |
A note on reading order: If you want one additional source, read chapters 2 and 3 of Hyndman & Athanasopoulos's Forecasting: Principles and Practice. They cover time series visualization and decomposition with the best combination of depth and accessibility. For historical context on the warming stripes, visit showyourstripes.info and read Ed Hawkins's original 2018 Twitter thread.