Statsmodels TSA Reference

Quick lookup for the statsmodels.tsa API — the core library for classical time series analysis in Python.

Key Classes

Class	Import	Purpose
ARIMA	from statsmodels.tsa.arima.model import ARIMA	Non-seasonal ARIMA models
SARIMAX	from statsmodels.tsa.statespace.sarimax import SARIMAX	Seasonal ARIMA with exogenous variables
ExponentialSmoothing	from statsmodels.tsa.holtwinters import ExponentialSmoothing	Holt-Winters exponential smoothing
SimpleExpSmoothing	from statsmodels.tsa.holtwinters import SimpleExpSmoothing	Single exponential smoothing (no trend/seasonality)

Key Functions

Function	Import	Purpose
adfuller	from statsmodels.tsa.stattools import adfuller	Augmented Dickey-Fuller test for stationarity
seasonal_decompose	from statsmodels.tsa.seasonal import seasonal_decompose	Decompose into trend, seasonal, residual
plot_acf	from statsmodels.graphics.tsaplots import plot_acf	Plot autocorrelation function
plot_pacf	from statsmodels.graphics.tsaplots import plot_pacf	Plot partial autocorrelation function

Common Workflow

import pandas as pd
import numpy as np
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.arima.model import ARIMA

# 1. Create / load time series
dates = pd.date_range("2020-01-01", periods=120, freq="MS")
ts = pd.Series(np.random.default_rng(42).normal(0, 1, 120).cumsum(), index=dates)

# 2. Test for stationarity
result = adfuller(ts)
print(f"ADF Statistic: {result[0]:.4f}")
print(f"p-value: {result[1]:.4f}")

# 3. Difference if non-stationary (p > 0.05)
if result[1] > 0.05:
    ts_diff = ts.diff().dropna()
    print("Series differenced once.")

# 4. Fit ARIMA
model = ARIMA(ts, order=(1, 1, 1))
fitted = model.fit()
print(fitted.summary())

# 5. Forecast
forecast = fitted.forecast(steps=12)
print(forecast)

KSB Mapping

KSB	Description	How This Addresses It
K4.1	Statistical models and methods	ARIMA, SARIMA, and exponential smoothing foundations
K4.2	Predictive analytics and ML techniques	Time series forecasting and model comparison
K5.3	Common patterns in real-world data	Identifying trends, seasonality, and stationarity
S1	Scientific methods and hypothesis testing	Stationarity testing, model diagnostics, forecast validation
S4	Analysis and models to inform outcomes	Building forecasts to support business planning
B5	Impartial, hypothesis-driven approach	Honest evaluation of forecast accuracy and limitations