diff --git a/plots/line-timeseries-rolling/implementations/bokeh.py b/plots/line-timeseries-rolling/implementations/bokeh.py
new file mode 100644
index 0000000000..791cd6ecb5
--- /dev/null
+++ b/plots/line-timeseries-rolling/implementations/bokeh.py
@@ -0,0 +1,92 @@
+""" pyplots.ai
+line-timeseries-rolling: Time Series with Rolling Average Overlay
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from bokeh.io import export_png, output_file, save
+from bokeh.models import ColumnDataSource
+from bokeh.plotting import figure
+
+
+# Data - Simulated daily temperature readings over a year
+np.random.seed(42)
+n_days = 365
+dates = pd.date_range("2024-01-01", periods=n_days, freq="D")
+
+# Create realistic temperature data with seasonal pattern and noise
+day_of_year = np.arange(n_days)
+seasonal_pattern = 15 * np.sin(2 * np.pi * (day_of_year - 80) / 365) + 20  # Peak in summer
+noise = np.random.normal(0, 3, n_days)
+temperature = seasonal_pattern + noise
+
+# Calculate 30-day rolling average
+rolling_window = 30
+rolling_avg = pd.Series(temperature).rolling(window=rolling_window, center=True).mean()
+
+# Create DataFrame
+df = pd.DataFrame({"date": dates, "value": temperature, "rolling_avg": rolling_avg})
+
+# Create ColumnDataSource for raw data
+source_raw = ColumnDataSource(data={"date": df["date"], "value": df["value"]})
+
+# Create ColumnDataSource for rolling average (exclude NaN values)
+df_rolling = df.dropna(subset=["rolling_avg"])
+source_rolling = ColumnDataSource(data={"date": df_rolling["date"], "rolling_avg": df_rolling["rolling_avg"]})
+
+# Create figure - 4800 × 2700 px (16:9 landscape)
+p = figure(
+    width=4800,
+    height=2700,
+    title="line-timeseries-rolling · bokeh · pyplots.ai",
+    x_axis_label="Date",
+    y_axis_label="Temperature (°C)",
+    x_axis_type="datetime",
+    tools="pan,wheel_zoom,box_zoom,reset,save",
+)
+
+# Plot raw data - thin line with transparency
+raw_line = p.line(
+    x="date", y="value", source=source_raw, line_width=2, line_alpha=0.5, line_color="#306998", legend_label="Raw Data"
+)
+
+# Plot rolling average - prominent smooth line
+rolling_line = p.line(
+    x="date",
+    y="rolling_avg",
+    source=source_rolling,
+    line_width=4,
+    line_color="#FFD43B",
+    legend_label=f"{rolling_window}-Day Rolling Average",
+)
+
+# Styling for large canvas
+p.title.text_font_size = "28pt"
+p.xaxis.axis_label_text_font_size = "22pt"
+p.yaxis.axis_label_text_font_size = "22pt"
+p.xaxis.major_label_text_font_size = "18pt"
+p.yaxis.major_label_text_font_size = "18pt"
+
+# Grid styling - subtle
+p.xgrid.grid_line_alpha = 0.3
+p.ygrid.grid_line_alpha = 0.3
+p.xgrid.grid_line_dash = [6, 4]
+p.ygrid.grid_line_dash = [6, 4]
+
+# Legend styling
+p.legend.label_text_font_size = "18pt"
+p.legend.location = "top_left"
+p.legend.background_fill_alpha = 0.8
+p.legend.border_line_alpha = 0.3
+
+# Background
+p.background_fill_color = "#fafafa"
+
+# Save as PNG
+export_png(p, filename="plot.png")
+
+# Save as HTML for interactive version
+output_file("plot.html")
+save(p)
diff --git a/plots/line-timeseries-rolling/metadata/bokeh.yaml b/plots/line-timeseries-rolling/metadata/bokeh.yaml
new file mode 100644
index 0000000000..cc2189c191
--- /dev/null
+++ b/plots/line-timeseries-rolling/metadata/bokeh.yaml
@@ -0,0 +1,29 @@
+library: bokeh
+specification_id: line-timeseries-rolling
+created: '2025-12-30T17:48:24Z'
+updated: '2025-12-30T17:56:16Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20602451770
+issue: 0
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-timeseries-rolling/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-timeseries-rolling/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/line-timeseries-rolling/bokeh/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent visual distinction between raw data (blue, semi-transparent) and rolling
+    average (yellow, prominent)
+  - Realistic temperature scenario with clear seasonal pattern that demonstrates the
+    value of rolling averages
+  - Proper handling of NaN values at rolling window boundaries by creating separate
+    data sources
+  - Interactive tools included (pan, wheel_zoom, box_zoom, reset, save) and HTML output
+    generated
+  - Clean KISS code structure following Bokeh best practices with ColumnDataSource
+  weaknesses:
+  - Legend text appears small at the top left corner when viewing the full-resolution
+    image
+  - Could add HoverTool to display exact date and temperature values on hover for
+    better interactivity