Explain ARIMA model for time series forecasting in R

This recipe explains what ARIMA model for time series forecasting in R

Recipe Objective

Explain ARIMA model for time series forecasting 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. This recipe demonstrates an example of ARIMA model for time series forecasting.

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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 auto.arima()

# Fitting model using auto.arima model
model <- auto.arima(rain_ts)
model

Step 5 - Make predictions

# Next 10 forecasted values
forecast_data <- forecast(model, 10)
print(forecast_data)
plot(forecast_data, main = "forecasting_data for rain_ts")

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