Locally Estimated Scatterplot Smoothing (LOESS)

Seasonal-Trend-LOESS (STL) Decomposition

• Variety seasonal pattern
• Handle outliers in time series data
• Only handle additive data

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Using airline passenger dataset (monthly totals)
url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv'
df = pd.read_csv(url, parse_dates=['Month'], index_col='Month')
df

	Passengers
Month
1949-01-01	112
1949-02-01	118
1949-03-01	132
1949-04-01	129
1949-05-01	121
...	...
1960-08-01	606
1960-09-01	508
1960-10-01	461
1960-11-01	390
1960-12-01	432

144 rows × 1 columns

df["Passengers"].plot(figsize = (12,5))

from statsmodels.tsa.seasonal import STL

res = STL(df["Passengers"]).fit()

res.plot()
plt.figure(figsize = (12,5))
plt.show()

<Figure size 1200x500 with 0 Axes>