Interpreting R Forecast Results

When using the forecast package in R for time series forecasting, you can view the prediction results by following these steps:

1. First, utilize the forecast() function to predict the time series data and store the results in an object, such as forecast_result.
2. Use the summary() function to view overall information about the prediction results, including predicted values, confidence intervals, and more.
3. Plot() function can be used to generate a visual representation of the predicted results, allowing for a clear comparison between predicted and actual values.
4. Use the accuracy() function to calculate metrics such as MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) to evaluate the performance of the prediction model.
5. You can examine the specific values of the forecast by extracting specific properties of the forecast_result object, such as $mean, $lower, and $upper, which represent the mean value and confidence interval of the predictions.

These steps allow for a comprehensive understanding and evaluation of the performance of time series forecasting models.