Time Series with Rolling Average Overlay in Python, R, Julia and JavaScript

Source code in 15 libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, plotnine, Pygal, Seaborn; R: ggplot2; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts.

A time series plot that displays raw data points alongside a smoothed rolling average (moving average) line. The raw data shows actual observations while the rolling average reveals underlying trends by reducing noise and short-term fluctuations. This dual-layer visualization is essential for trend identification, making patterns visible that might be obscured by day-to-day volatility.

Renders

• Full-resolution render, light theme
• Full-resolution render, dark theme
• Interactive version, light theme
• Interactive version, dark theme

Preview render from the MUI X Charts implementation.

Implementations

• Time Series with Rolling Average Overlay in Altair (Python)
• Time Series with Rolling Average Overlay in Bokeh (Python)
• Time Series with Rolling Average Overlay in lets-plot (Python)
• Time Series with Rolling Average Overlay in Matplotlib (Python)
• Time Series with Rolling Average Overlay in Plotly (Python)
• Time Series with Rolling Average Overlay in plotnine (Python)
• Time Series with Rolling Average Overlay in Pygal (Python)
• Time Series with Rolling Average Overlay in Seaborn (Python)
• Time Series with Rolling Average Overlay in ggplot2 (R)
• Time Series with Rolling Average Overlay in Makie.jl (Julia)
• Time Series with Rolling Average Overlay in Chart.js (JavaScript)
• Time Series with Rolling Average Overlay in D3.js (JavaScript)
• Time Series with Rolling Average Overlay in Apache ECharts (JavaScript)
• Time Series with Rolling Average Overlay in Highcharts (JavaScript)
• Time Series with Rolling Average Overlay in MUI X Charts (JavaScript)