Extrapolation
Estimating values outside the range of observed data, often used in spatial prediction and modelling (standard GIS usage).
What is the purpose of Extrapolation?
By extending current trends or patterns, extrapolation is the technique of estimating or forecasting values outside of the known range of data. Forecasting future values based on current or historical data is a popular practice in data analysis, science, statistics, and mathematics.
Key purposes of extrapolation include:
Predicting future results is used to forecast trends (e.g., future sales, temperature, or population increase) in domains including business, economics, weather, and demographic research.
Filling in the gaps: This aids in estimating values in situations when there is a lack of direct data, particularly when measurements are scarce.
Making educated decisions: This helps researchers, analysts, and planners make educated guesses about potential future events in order to allocate resources and make better decisions.
Scientific and technical analysis: Extrapolation, based on known patterns, can be used in experiments or models to estimate results under untested conditions.
Related Keywords
Data extrapolation methods use current trends to estimate values beyond observed data. Time series extrapolation, linear extrapolation, and polynomial extrapolation are common techniques. Although they can be less dependable outside of the given data range, they are helpful for forecasting.
Linear extrapolation estimates a value beyond known data by extending a trend linearly. Formula:
y = y_1 + \frac{(x - x_1)}{(x_2 - x_1)} \cdot (y_2 - y_1)
Here, (x_1, y_1) and (x_2, y_2) are known points, and y is the predicted value at x.
Extrapolation is the technique of forecasting values outside of the observed data range using current trends in data science. Although it can be dangerous since it could produce erroneous results if the trend changes, it makes the assumption that patterns in the known data persist beyond the observed range.
While extrapolation estimates values outside of the known data point range, interpolation estimates values within the known data point range. Extrapolation is riskier since it believes the trend continues outside the known range, whereas interpolation exploits existing patterns within recorded data, making it generally more dependable.