Point forecasts and interval forecasts are two common ways to predict future values, but they serve different purposes and convey distinct types of information. A point forecast provides a single estimated value for a future event, such as predicting that tomorrow’s temperature will be 25°C or that next month’s sales will be 10,000 units. It’s a straightforward, concrete number that represents the “best guess” from a model. In contrast, an interval forecast gives a range of possible values, along with a probability that the actual value will fall within that range. For example, it might state there’s a 90% chance sales will be between 8,500 and 11,500 units next month. This range reflects uncertainty in the prediction, offering a more nuanced view of potential outcomes.
The key difference lies in how they handle uncertainty. Point forecasts collapse all information into a single value, which can be useful for simple decision-making but hides the model’s confidence. For instance, a weather app showing 25°C doesn’t indicate whether the temperature might realistically swing between 20°C and 30°C. Interval forecasts, however, explicitly quantify uncertainty, making them valuable in scenarios where risk matters. Developers working on systems like inventory management or resource allocation might prefer interval forecasts because they reveal worst-case and best-case scenarios. For example, a cloud service predicting server load might use a 95% confidence interval to ensure capacity covers a range of possible user traffic, avoiding costly overprovisioning or underprovisioning.
From a technical perspective, generating these forecasts involves different methods. Point forecasts often come from algorithms like linear regression or ARIMA, which optimize for minimizing average error (e.g., mean squared error). Interval forecasts require estimating the distribution of possible outcomes, often using techniques like quantile regression, bootstrapping, or Bayesian methods. For developers, implementing interval forecasts might involve libraries like statsmodels (for confidence intervals) or tools like Prophet (which provides uncertainty intervals by default). While point forecasts are simpler to compute and interpret, interval forecasts add computational overhead but provide critical context for decisions requiring risk assessment. Choosing between them depends on the problem: use point forecasts when a single actionable value suffices, and interval forecasts when understanding variability is essential.