Auto ARIMA in Python
In this article, we will learn about Auto ARIMA in Python and how it works.
Auto ARIMA in Python
The auto_arima() function from the pmdarima library assists in determining the ARIMA model’s optimum parameters and provides a fitted ARIMA model as a result.
Take note that this package was once called "Pyramid" before being renamed "pmdarima". Ensure that the "pmdarima" package is being installed.
If you don’t have the package, install it by running the below command in the terminal.
pip install pmdarima
Use the following command to test see whether the package was created successfully.
from pmdarima.arima import auto_arima
Use the auto_arima() Function in Python
In the following code, the data.csv is a CSV file containing the data and is used for Auto ARIMA. The output will be dataframe with a value with order=(P,D,Q) in index p and q.
Code Example:
import pmdarima as pm
import pandas as pd
df1 = pd.read_csv("data.csv", names=["value"], header=0)
model_1 = pm.auto_arima(
df1.value,
start_p=1,
start_q=1,
test="adf",
max_p=3,
max_q=3,
m=1,
d=None,
seasonal=False,
start_P=0,
D=0,
trace=True,
error_action="ignore",
suppress_warnings=True,
stepwise=True,
)
print(model_1.summary())
Output:
Performing stepwise search to minimize aic
ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=1605.366, Time=0.09 sec
ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=1660.860, Time=0.01 sec
ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=1619.269, Time=0.04 sec
ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=1604.209, Time=0.04 sec
ARIMA(0,1,0)(0,0,0)[0] : AIC=1658.968, Time=0.01 sec
ARIMA(0,1,2)(0,0,0)[0] intercept : AIC=1605.215, Time=0.08 sec
ARIMA(1,1,2)(0,0,0)[0] intercept : AIC=1606.845, Time=0.12 sec
ARIMA(0,1,1)(0,0,0)[0] : AIC=1603.295, Time=0.02 sec
ARIMA(1,1,1)(0,0,0)[0] : AIC=1604.373, Time=0.03 sec
ARIMA(0,1,2)(0,0,0)[0] : AIC=1604.196, Time=0.04 sec
ARIMA(1,1,0)(0,0,0)[0] : AIC=1617.588, Time=0.04 sec
ARIMA(1,1,2)(0,0,0)[0] : AIC=1605.883, Time=0.04 sec
Best model: ARIMA(0,1,1)(0,0,0)[0]
Total fit time: 0.580 seconds
SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 173
Model: SARIMAX(0, 1, 1) Log Likelihood -799.648
Date: Sat, 03 Sep 2022 AIC 1603.295
Time: 23:15:18 BIC 1609.590
Sample: 0 HQIC 1605.849
- 173
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ma.L1 -0.5856 0.056 -10.478 0.000 -0.695 -0.476
sigma2 637.6579 54.893 11.616 0.000 530.069 745.247
===================================================================================
Ljung-Box (L1) (Q): 0.54 Jarque-Bera (JB): 24.81
Prob(Q): 0.46 Prob(JB): 0.00
Heteroskedasticity (H): 0.18 Skew: 0.41
Prob(H) (two-sided): 0.00 Kurtosis: 4.67
===================================================================================
Process finished with exit code 0
Conclusion
The ARIMA model extensively estimates the stock performance over the next several days. The auto_arima() function of Python is used to identify the optimum parameters of the fitted ARIMA model.
The auto_arima() function can be imported from the Python library named pmdarima.
Zeeshan is a detail oriented software engineer that helps companies and individuals make their lives and easier with software solutions.
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