How to find optimal paramters for ARIMA model?
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How to find optimal paramters for ARIMA model?

How to find optimal paramters for ARIMA model?

This recipe helps you find optimal paramters for ARIMA model

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Recipe Objective

The ARIMA model for time series analysis and forecasting can be tricky to configure. We can automate the process of evaluating a large number of hyperparameters for the ARIMA model by using a grid search procedure.

So this recipe is a short example on how to find optimal paramters for ARIMA model. Let's get started.

Step 1 - Import the library

import warnings import numpy as np import pandas as pd from statsmodels.tsa.arima_model import ARIMA from sklearn.metrics import mean_squared_error

Let's pause and look at these imports. Numpy, pandas and warnings are general ones. Here, statsmodels.tsa.arima_model will help in building our model. mean_squared_error will be used for calculating MSE score.

Step 2 - Setup the Data

df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date']).set_index('date')

Here, we have used one time series data from github. Also, we have set our index to date.

Now our dataset is ready.

Step 3 - Splitting Dataset

train_data = df[1:len(df)-12] test_data = df[len(df)-12:]

Here, we have simply broken our dataset to two parts as test and train.

Step 4 - GridSearch

p_values = [0, 1] d_values = range(0, 2) q_values = range(0, 2)

Here, we have defined p,d and q for hyperparameter testing.

Step 5 - Looping for testing

for p in p_values: for d in d_values: for q in q_values: order = (p,d,q) warnings.filterwarnings("ignore") model = ARIMA(train_data.value, order=order).fit() predictions = model.predict(start=len(train_data), end=len(train_data) + len(test_data)-1) error = mean_squared_error(test_data, predictions) print('ARIMA%s MSE=%.3f' % (order,error))

With each loop, we choose one parameter, fit the model and calculate the MSE over predictions. Later we choose the best model by looking at lowest MSE score.

Step 6 - Lets look at our dataset now

Once we run the above code snippet, we will see:

Srcoll down the ipython file to visualize the results.

Best model to choose is (1,0,1).

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