How to evaluate time series models using BIC in R

This recipe helps you evaluate time series models using BIC in R

Recipe Objective

How to evaluate time series models using BIC?

Time series is a statistical technique that deals with time series data or trend analysis. The BIC: The Bayesian Information Criterion is an index used in Bayesian statistics to choose between two or more models. Models are compared with the Bayesian information criterion by calculating the BIC for each model. The smaller the BIC value, the better the time series model. This recipe demonstrates an example of how to evaluate time series models using BIC.

Learn Time Series Forecasting using ARIMA Model in Python

Step 1 - Install required package

install.packages('forecast') library(forecast)

Step 2 - Generate random time series data

# Get the data points in form of a R vector. rain <- c(987,1025,978,774,1563,569,1456,789,1479,566,1563,1698) # Convert it to a time series object. rain_ts <- ts(rain,start = c(2020,1),frequency = 12) # Print the timeseries data. print(rain_ts) plot(rain_ts, main = "Time series data") summary(rain_ts)

Step 3 - Build a model using arima()

model1 <- arima(rain_ts, order = c(1,0,0)) model1 model2 <- arima(rain_ts, order = c(2,0,0)) model2

We can try to fit different 'ARIMA models' by changing the order. The lower the BIC term , the better the model. Hence we choose the a model with order - (1,0,0)

Step 4 - Calculate the BIC values

BIC(model1) BIC(model2)

Clearly the model1 with arima parameters (1,0,0) is better then model2 as the BIC value of model 1 (180.85) is lesser then BIC of model 2 (183.1)

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