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.
""" anyplot.ai
timeseries-decomposition: Time Series Decomposition Plot
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-14
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
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"
# Okabe-Ito palette
IMPRINT = [
"#009E73", # bluish green (brand — first series)
"#C475FD", # vermillion
"#4467A3", # blue
"#BD8233", # reddish purple
]
# Data - Monthly retail sales over 6 years (72 months = 6 full annual cycles)
np.random.seed(42)
n_months = 72
dates = pd.date_range(start="2018-01-01", periods=n_months, freq="MS")
# Create realistic retail sales data with trend, seasonality, and noise
trend = np.linspace(100, 180, n_months) + np.cumsum(np.random.randn(n_months) * 0.5)
seasonal = 25 * np.sin(2 * np.pi * np.arange(n_months) / 12) # Annual cycle
# Add holiday bump in December (month 12)
holiday_bump = np.array([15 if (i + 1) % 12 == 0 else 0 for i in range(n_months)])
seasonal = seasonal + holiday_bump
residual = np.random.randn(n_months) * 8
values = trend + seasonal + residual
# Create time series
ts = pd.Series(values, index=dates)
# Perform seasonal decomposition (additive model)
decomposition = seasonal_decompose(ts, model="additive", period=12)
# Create plot with 4 subplots
fig, axes = plt.subplots(4, 1, figsize=(16, 12), sharex=True, facecolor=PAGE_BG)
# Original series
axes[0].plot(dates, ts.values, color=IMPRINT[0], linewidth=2.5)
axes[0].set_facecolor(PAGE_BG)
axes[0].set_ylabel("Original (Sales USD)", fontsize=20, color=INK)
axes[0].tick_params(axis="y", labelsize=16, colors=INK_SOFT)
axes[0].grid(True, alpha=0.15, linewidth=0.8, color=INK)
axes[0].set_title(
"timeseries-decomposition · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK, pad=15
)
# Trend component
axes[1].plot(dates, decomposition.trend, color=IMPRINT[1], linewidth=2.5)
axes[1].set_facecolor(PAGE_BG)
axes[1].set_ylabel("Trend (Sales USD)", fontsize=20, color=INK)
axes[1].tick_params(axis="y", labelsize=16, colors=INK_SOFT)
axes[1].grid(True, alpha=0.15, linewidth=0.8, color=INK)
# Seasonal component
axes[2].plot(dates, decomposition.seasonal, color=IMPRINT[2], linewidth=2.5)
axes[2].set_facecolor(PAGE_BG)
axes[2].set_ylabel("Seasonal (Sales USD)", fontsize=20, color=INK)
axes[2].tick_params(axis="y", labelsize=16, colors=INK_SOFT)
axes[2].grid(True, alpha=0.15, linewidth=0.8, color=INK)
# Residual component
axes[3].plot(dates, decomposition.resid, color=IMPRINT[3], linewidth=2.5)
axes[3].axhline(y=0, color=INK_SOFT, linestyle="-", linewidth=1, alpha=0.3)
axes[3].set_facecolor(PAGE_BG)
axes[3].set_ylabel("Residual (Sales USD)", fontsize=20, color=INK)
axes[3].set_xlabel("Date", fontsize=20, color=INK)
axes[3].tick_params(axis="both", labelsize=16, colors=INK_SOFT)
axes[3].grid(True, alpha=0.15, linewidth=0.8, color=INK)
# Remove top and right spines
for ax in axes:
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(INK_SOFT)
ax.spines["bottom"].set_color(INK_SOFT)
# Adjust x-axis tick formatting
fig.autofmt_xdate(rotation=45, ha="right")
fig.axes[-1].tick_params(axis="x", labelsize=16)
# Adjust spacing between subplots
plt.tight_layout()
plt.subplots_adjust(hspace=0.15)
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Time Series Decomposition Plot on anyplot.ai.