Extrapolative methods

The tendency is to make greater use of qualitative indicators. Linear extrapolation. This means creating a tangent line at the end of the known data and extending it beyond that limit. A linear extrapolation will only provide good results when used to extend the graph of an approximately linear function. Circular (constant curvature) extrapolation. In addition to maintaining an angle match at the end of the known data, the curvature may also be matched. Note that this type of extrapolation will curve back on itself eventually, so is only good for a short region. Conic extrapolation. A conic section can be created using five points near the end of the known data. If the conic section created is an ellipse or circle, it will curve back on itself. A parabolic or hyperbolic curve will not, but may curve back relative to the X-axis. Polynomial extrapolation. A polynomial curve be extended and be created through the entire known data or just near the end. Polynomial extrapolation is typically done by means of Newton's method of finite differences to create a Newton series that fits the data. The resulting polynomial may be used to extrapolate the data.