How to evaluate timeseries models using BIC?
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How to evaluate timeseries models using BIC?

How to evaluate timeseries models using BIC?

This recipe helps you evaluate timeseries models using BIC

0

Recipe Objective

The Bayesian Information Criterion (BIC) is an index used in Bayesian statistics to choose between two or more alternative models. Comparing models with the Bayesian information criterion simply involves calculating the BIC for each model. The model with the lowest BIC is considered the best.

So this recipe is a short example on how to evaluate time series models using BIC. Let's get started.

Step 1 - Import the library

import numpy as np import pandas as pd from statsmodels.tsa.arima_model import ARIMA

Let's pause and look at these imports. Numpy and pandas are general ones. Here matplotlib.pyplot will help us in plotting. statsmodels.tsa.arima_model will help us in model building.

Step 2 - Setup the Data

df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['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 - Calculating BIC

for i in range(0,2): for j in range(0,2): for k in range(0,2): model = ARIMA(df.value, order=(i, j, k)).fit() print(model.bic)

Best BIC can easily be calcuated through libraries. Here we have tried to understand what actually is happening inside. With variation of values of orders, BIC can be seen varying.

Step 4 - Lets look at our dataset now

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

1316.66388768731
1162.0554484438037
913.5172157072849
868.8258162104078
918.9268418564968
888.116114839384
889.526006058412
857.0907664191164

Clearly, order (1,1,1) is best fitted solution to our model. It can be extended further to 2 degrees to have a better understanding of results.

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