How to find optimal parameters for ARIMA model in R

This recipe helps you find optimal parameters for ARIMA model in R

Recipe Objective

How to find optimal parameters for ARIMA model in R?

Time series is a statistical technique that deals with time series data or trend analysis. Time series data means the data is collected over a period of time/ intervals. Time series data helps us with making forecasting based on the previously collected data. ARIMA is a time series forecasting method where AR stands for Autoregressive and MA stands for Moving Average. Here we are going to define the parameters (p,d,q) for ARIMA modelling. This recipe demonstrates an example of finding optimal parameters for the ARIMA model.

Learn About the Application of ARCH and GARCH models in Real-World

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)
summary(rain_ts)

Step 3 - Plot a data

plot(rain_ts,main = "Before prediction")

Step 4 - Build a model using arima()

# Fitting model using arima model

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

How to choose the optimal parameters for the arima model We can try to fit different 'arima models' by changing the order/parameters by different values of parameters to find the optimal parameters. The lower the aic term, the better the model. Hence, we choose the model with order - (1,0,0)

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