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: seaborn 0.13.2 | Python 3.13.13
Quality: 97/100 | Updated: 2026-05-14
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
# 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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
RULE = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
BRAND = "#009E73"
COLOR_TREND = "#C475FD"
COLOR_SEASONAL = "#4467A3"
COLOR_RESIDUAL = "#BD8233"
# Set seaborn theme with theme-adaptive colors
sns.set_theme(
style="ticks",
rc={
"figure.facecolor": PAGE_BG,
"axes.facecolor": PAGE_BG,
"axes.edgecolor": INK_SOFT,
"axes.labelcolor": INK,
"text.color": INK,
"xtick.color": INK_SOFT,
"ytick.color": INK_SOFT,
"grid.color": INK,
"grid.alpha": 0.10,
"legend.facecolor": ELEVATED_BG,
"legend.edgecolor": INK_SOFT,
},
)
# Data - Monthly electricity consumption (kWh)
np.random.seed(42)
n_years = 8
n_months = n_years * 12
period = 12
# Create date range
dates = pd.date_range(start="2016-01-01", periods=n_months, freq="MS")
# Generate realistic electricity consumption with trend, seasonality, and noise
# Trend: growing demand over years (5000 to 7500 kWh)
trend_component = np.linspace(5000, 7500, n_months)
# Seasonality: higher in summer/winter (AC/heating), lower in spring/fall
seasonal_pattern = np.array([1.25, 1.20, 1.05, 0.95, 0.85, 0.90, 1.10, 1.15, 1.05, 0.95, 1.10, 1.20])
seasonal_component = np.tile(seasonal_pattern, n_years)
# Noise
noise = np.random.normal(0, 100, n_months)
# Multiplicative model
values = trend_component * seasonal_component + noise
# Create DataFrame
df = pd.DataFrame({"date": dates, "consumption": values})
df = df.set_index("date")
# Manual seasonal decomposition (additive model)
trend = df["consumption"].rolling(window=period, center=True, min_periods=1).mean()
detrended = df["consumption"] - trend
seasonal = detrended.groupby(detrended.index.month).transform("mean")
residual = df["consumption"] - trend - seasonal
# Create figure with 4 subplots
fig, axes = plt.subplots(4, 1, figsize=(16, 9), sharex=True)
fig.patch.set_facecolor(PAGE_BG)
for ax in axes:
ax.set_facecolor(PAGE_BG)
fig.subplots_adjust(hspace=0.25)
# Plot 1: Original
sns.lineplot(x=df.index, y=df["consumption"], ax=axes[0], color=BRAND, linewidth=2.5, legend=False)
axes[0].set_ylabel("Original (kWh)", fontsize=20, color=INK)
axes[0].set_title("Original", fontsize=22, fontweight="medium", loc="left", color=INK)
axes[0].tick_params(axis="both", labelsize=16, colors=INK_SOFT)
axes[0].grid(True, alpha=0.1, linewidth=0.8)
axes[0].spines["top"].set_visible(False)
axes[0].spines["right"].set_visible(False)
for spine in ("left", "bottom"):
axes[0].spines[spine].set_color(INK_SOFT)
# Plot 2: Trend
sns.lineplot(x=trend.index, y=trend.values, ax=axes[1], color=COLOR_TREND, linewidth=2.5, legend=False)
axes[1].set_ylabel("Trend (kWh)", fontsize=20, color=INK)
axes[1].set_title("Trend", fontsize=22, fontweight="medium", loc="left", color=INK)
axes[1].tick_params(axis="both", labelsize=16, colors=INK_SOFT)
axes[1].grid(True, alpha=0.1, linewidth=0.8)
axes[1].spines["top"].set_visible(False)
axes[1].spines["right"].set_visible(False)
for spine in ("left", "bottom"):
axes[1].spines[spine].set_color(INK_SOFT)
# Plot 3: Seasonal
sns.lineplot(x=seasonal.index, y=seasonal.values, ax=axes[2], color=COLOR_SEASONAL, linewidth=2.5, legend=False)
axes[2].axhline(y=0, color=INK_SOFT, linestyle="--", linewidth=1, alpha=0.5)
axes[2].set_ylabel("Seasonal (kWh)", fontsize=20, color=INK)
axes[2].set_title("Seasonal", fontsize=22, fontweight="medium", loc="left", color=INK)
axes[2].tick_params(axis="both", labelsize=16, colors=INK_SOFT)
axes[2].grid(True, alpha=0.1, linewidth=0.8)
axes[2].spines["top"].set_visible(False)
axes[2].spines["right"].set_visible(False)
for spine in ("left", "bottom"):
axes[2].spines[spine].set_color(INK_SOFT)
# Plot 4: Residual
sns.lineplot(x=residual.index, y=residual.values, ax=axes[3], color=COLOR_RESIDUAL, linewidth=2.0, legend=False)
axes[3].axhline(y=0, color=INK_SOFT, linestyle="--", linewidth=1, alpha=0.5)
axes[3].set_ylabel("Residual (kWh)", fontsize=20, color=INK)
axes[3].set_xlabel("Date", fontsize=20, color=INK)
axes[3].set_title("Residual", fontsize=22, fontweight="medium", loc="left", color=INK)
axes[3].tick_params(axis="both", labelsize=16, colors=INK_SOFT)
axes[3].grid(True, alpha=0.1, linewidth=0.8)
axes[3].spines["top"].set_visible(False)
axes[3].spines["right"].set_visible(False)
for spine in ("left", "bottom"):
axes[3].spines[spine].set_color(INK_SOFT)
# Main title
fig.suptitle("timeseries-decomposition · seaborn · anyplot.ai", fontsize=24, fontweight="medium", y=0.995, color=INK)
plt.tight_layout(rect=[0, 0, 1, 0.99])
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Part of Time Series Decomposition Plot on anyplot.ai.