Important Code snippet
Performing a time series forecast using the ARIMA model:
```Runemployment_ts <- ts(merged_data$Unemployment_Rate, start = 2000, end = 2022, frequency = 1)
fit <- auto.arima(unemployment_ts)
forecasted_values <- forecast(fit, h = 3)
autoplot(forecasted_values) + labs(title = "Unemployment Rate Forecast",
x = "Year",
y = "Unemployment Rate") +
theme_minimal()
Results
The ARIMA model forecasts the unemployment rate for the next 3 years: 2023, 2024, and 2025, based on historical data from 2000 to 2022. The predictions assume that past trends will continue. While reliable for short-term forecasting, unexpected economic events may cause deviations. In conclusion, this ARIMA model offers a reasonable estimate of future unemployment rates, but its accuracy depends on how closely future conditions match past trends.