feat(plotly): implement timeseries-decomposition (#3019)

## Implementation: `timeseries-decomposition` - plotly

Implements the **plotly** version of `timeseries-decomposition`.

**File:** `plots/timeseries-decomposition/implementations/plotly.py`

**Parent Issue:** #2992

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:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20617478986)*

---------

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/timeseries-decomposition/implementations/plotly.py

@@ -0,0 +1,126 @@
+""" pyplots.ai
+timeseries-decomposition: Time Series Decomposition Plot
+Library: plotly 6.5.0 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+from statsmodels.tsa.seasonal import seasonal_decompose
+
+
+# Data: Monthly airline passengers (classic time series with trend and seasonality)
+np.random.seed(42)
+
+# Create 10 years of monthly data (120 points)
+dates = pd.date_range(start="2014-01-01", periods=120, freq="MS")
+
+# Generate realistic airline passenger data with trend, seasonality, and noise
+trend = np.linspace(100, 250, 120)  # Growing trend
+seasonal = 30 * np.sin(2 * np.pi * np.arange(120) / 12)  # Annual cycle (peak in summer)
+noise = np.random.normal(0, 10, 120)
+passengers = trend + seasonal + noise
+
+# Create time series and decompose
+ts = pd.Series(passengers, index=dates)
+decomposition = seasonal_decompose(ts, model="additive", period=12)
+
+# Create subplots (4 rows, shared x-axis)
+fig = make_subplots(
+    rows=4,
+    cols=1,
+    shared_xaxes=True,
+    vertical_spacing=0.08,
+    subplot_titles=("Original", "Trend", "Seasonal", "Residual"),
+)
+
+# Color scheme
+primary_color = "#306998"  # Python Blue
+secondary_color = "#FFD43B"  # Python Yellow
+
+# Original series
+fig.add_trace(
+    go.Scatter(x=dates, y=ts.values, mode="lines", line=dict(color=primary_color, width=2.5), name="Original"),
+    row=1,
+    col=1,
+)
+
+# Trend component
+fig.add_trace(
+    go.Scatter(
+        x=dates,
+        y=decomposition.trend,
+        mode="lines",
+        line=dict(color="#2E8B57", width=3),  # Sea green for trend
+        name="Trend",
+    ),
+    row=2,
+    col=1,
+)
+
+# Seasonal component
+fig.add_trace(
+    go.Scatter(
+        x=dates,
+        y=decomposition.seasonal,
+        mode="lines",
+        line=dict(color="#E07020", width=2.5),  # Orange for seasonal
+        name="Seasonal",
+    ),
+    row=3,
+    col=1,
+)
+
+# Residual component
+fig.add_trace(
+    go.Scatter(
+        x=dates,
+        y=decomposition.resid,
+        mode="lines",
+        line=dict(color="#8B4789", width=2),  # Purple for residual
+        name="Residual",
+    ),
+    row=4,
+    col=1,
+)
+
+# Update layout for large canvas
+fig.update_layout(
+    title=dict(text="timeseries-decomposition · plotly · pyplots.ai", font=dict(size=32), x=0.5, xanchor="center"),
+    template="plotly_white",
+    showlegend=False,
+    height=900,
+    width=1600,
+    margin=dict(l=100, r=60, t=100, b=80),
+)
+
+# Update all y-axes
+y_axis_titles = ["Passengers (thousands)", "Trend", "Seasonal Component", "Residual"]
+for i, title in enumerate(y_axis_titles, 1):
+    fig.update_yaxes(
+        title=dict(text=title, font=dict(size=20)),
+        tickfont=dict(size=16),
+        gridcolor="rgba(128, 128, 128, 0.2)",
+        gridwidth=1,
+        row=i,
+        col=1,
+    )
+
+# Update x-axes
+for i in range(1, 5):
+    fig.update_xaxes(tickfont=dict(size=16), gridcolor="rgba(128, 128, 128, 0.2)", gridwidth=1, row=i, col=1)
+
+# Bottom x-axis label
+fig.update_xaxes(title=dict(text="Date", font=dict(size=20)), row=4, col=1)
+
+# Update subplot titles font size
+for annotation in fig.layout.annotations:
+    annotation.font.size = 22
+
+# Save as PNG (4800 x 2700 px)
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+
+# Save interactive HTML version
+fig.write_html("plot.html", include_plotlyjs=True, full_html=True)

plots/timeseries-decomposition/metadata/plotly.yaml

@@ -0,0 +1,25 @@
+library: plotly
+specification_id: timeseries-decomposition
+created: '2025-12-31T10:55:31Z'
+updated: '2025-12-31T11:53:53Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20617478986
+issue: 2992
+python_version: 3.13.11
+library_version: 6.5.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/timeseries-decomposition/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/timeseries-decomposition/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/timeseries-decomposition/plotly/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Clean four-panel layout with well-differentiated components using distinct colors
+  - Proper use of statsmodels seasonal_decompose for authentic time series decomposition
+  - 'Title follows required format exactly: timeseries-decomposition · plotly · pyplots.ai'
+  - Excellent use of Plotly subplots with shared x-axes for time alignment
+  - Realistic airline passenger data scenario with clear trend and seasonality
+  weaknesses:
+  - Y-axis label for Original subplot says Passengers (thousands) but other subplots
+    lack units context
+  - Trend subplot y-axis label just says Trend without indicating the unit is still
+    passengers