What is Prediction Bound in Modeling?

Prediction bounds in modeling, similar to confidence bounds, represent a range of values within which a model's predictions are expected to fall. However, prediction bounds focus on individual predictions or estimates for specific data points, rather than population parameters. These bounds provide a measure of the uncertainty and variability associated with individual predictions. Here's an overview of prediction bounds in modeling:

Prediction Interval (PI):

A prediction interval is a range of values within which you can reasonably predict that a future or individual data point will fall. It takes into account both the uncertainty in the model's parameters and the natural variability of the data.

Prediction intervals are typically calculated for specific predicted values, such as the forecast of a future time point in time series analysis or the prediction of an outcome for a specific set of predictor variables in regression analysis.

Prediction Bounds:

Prediction bounds refer to the lower and upper limits of the prediction interval. These bounds define the range of values where an individual prediction is likely to fall. The lower prediction bound (LPB) corresponds to the lower limit of the prediction interval, while the upper prediction bound (UPB) corresponds to the upper limit.


Prediction intervals and bounds are used to quantify the uncertainty in individual predictions. They provide a measure of the range within which a new data point or observation is expected to fall. For example, a 95% prediction interval for a time series forecast suggests that there is a 95% probability that the actual future data point will fall within that interval.


Prediction bounds are commonly used in various modeling scenarios, including time series forecasting, regression modeling, and any situation where individual predictions need to be assessed. In time series analysis, prediction bounds are vital for understanding the uncertainty in future data points.

In summary, prediction bounds in modeling provide a measure of the uncertainty associated with individual predictions. They are essential for assessing the reliability of model forecasts and making informed decisions based on individual predictions, especially in scenarios where data observations are subject to inherent variability.

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