Time Series Decomposition Plot — Plotly

A time series decomposition plot displays a time series broken down into its constituent components: the original series, trend, seasonal pattern, and residual noise. Each component is shown as a separate subplot stacked vertically, sharing a common time axis. This visualization is essential for understanding the underlying structure of time series data and identifying patterns that may not be visible in the raw series.

Time Series Decomposition Plot rendered with Plotly

Python source (Plotly)

""" anyplot.ai
timeseries-decomposition: Time Series Decomposition Plot
Library: plotly 6.7.0 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-14
"""

import os
import sys

sys.path.pop(0)

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

# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"

# Okabe-Ito palette
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233"]

# Data: Monthly airline passengers (classic time series)
np.random.seed(42)
dates = pd.date_range(start="2014-01-01", periods=120, freq="MS")
trend = np.linspace(100, 250, 120)
seasonal = 30 * np.sin(2 * np.pi * np.arange(120) / 12)
noise = np.random.normal(0, 10, 120)
passengers = trend + seasonal + noise

# Time series and decomposition
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"),
)

# Add traces for each component
fig.add_trace(
    go.Scatter(
        x=dates,
        y=ts.values,
        mode="lines",
        line=dict(color=IMPRINT[0], width=2.5),
        name="Original",
        hovertemplate="<b>Original</b><br>Date: %{x|%Y-%m}<br>Value: %{y:.1f}<extra></extra>",
    ),
    row=1,
    col=1,
)

fig.add_trace(
    go.Scatter(
        x=dates,
        y=decomposition.trend,
        mode="lines",
        line=dict(color=IMPRINT[1], width=3),
        name="Trend",
        hovertemplate="<b>Trend</b><br>Date: %{x|%Y-%m}<br>Value: %{y:.1f}<extra></extra>",
    ),
    row=2,
    col=1,
)

fig.add_trace(
    go.Scatter(
        x=dates,
        y=decomposition.seasonal,
        mode="lines",
        line=dict(color=IMPRINT[2], width=2.5),
        name="Seasonal",
        hovertemplate="<b>Seasonal</b><br>Date: %{x|%Y-%m}<br>Value: %{y:.1f}<extra></extra>",
    ),
    row=3,
    col=1,
)

fig.add_trace(
    go.Scatter(
        x=dates,
        y=decomposition.resid,
        mode="lines",
        line=dict(color=IMPRINT[3], width=2),
        name="Residual",
        hovertemplate="<b>Residual</b><br>Date: %{x|%Y-%m}<br>Value: %{y:.1f}<extra></extra>",
    ),
    row=4,
    col=1,
)

# Update layout
fig.update_layout(
    title=dict(text="timeseries-decomposition · plotly · anyplot.ai", font=dict(size=28, color=INK), x=0.5, xanchor="center"),
    paper_bgcolor=PAGE_BG,
    plot_bgcolor=PAGE_BG,
    font=dict(color=INK),
    showlegend=False,
    height=900,
    width=1600,
    margin=dict(l=100, r=60, t=100, b=80),
)

# Update all y-axes with theme-adaptive colors
y_axis_titles = ["Passengers", "Trend", "Seasonal", "Residual"]
for i, title in enumerate(y_axis_titles, 1):
    fig.update_yaxes(
        title=dict(text=title, font=dict(size=22, color=INK)),
        tickfont=dict(size=18, color=INK_SOFT),
        gridcolor=GRID,
        gridwidth=1,
        linecolor=INK_SOFT,
        row=i,
        col=1,
    )

# Update x-axes
for i in range(1, 5):
    fig.update_xaxes(
        tickfont=dict(size=18, color=INK_SOFT),
        gridcolor=GRID,
        gridwidth=1,
        linecolor=INK_SOFT,
        row=i,
        col=1,
    )

# Bottom x-axis label
fig.update_xaxes(title=dict(text="Date", font=dict(size=22, color=INK)), row=4, col=1)

# Update subplot titles font size and color
for annotation in fig.layout.annotations:
    annotation.font.size = 22
    annotation.font.color = INK

# Save as PNG and HTML
fig.write_image(f"plot-{THEME}.png", width=1600, height=900, scale=3)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")

Part of Time Series Decomposition Plot on anyplot.ai.

Time Series Decomposition Plot — Plotly

Python source (Plotly)

Other implementations