Interpolation & Prediction

The application is intended to interpolate real functions from a single variable. Functions are a set of points (X, Y). The following interpolation methods can be applied: Newton's, Aitken's, cubic Hermite's method, cardinal spline interpolation, Catmul-Rom's spline, Kochanek-Bartls's spline, linear interpolation and nearest neighbor interpolation. If the function is a time series, then methods for predicting and calculating autocorrelation may be applied to detect internal cycles. The following methods for statistical prediction are applied - an exponentially weighted moving average; - simple moving average; - linear exponential weighing; - Holt's linear exponential smoothing; and an additional slowing trend. The mean and the standard deviation of the forecast errors are calculated. The functions, the results of their processing and the forecasts can be stored in a database of type Sqlit or in selected folder . Tables with this data can be exported for printing, for example, using the Sqlit browser or by Internet. The application is intended to interpolate real functions from a single variable and for statistical prediction interpolate real functions(set of points (X, Y)) from a single variable can be applied interpolation methods: Newton's, Aitken's, cubic Hermite's, cardinal spline Catmul-Rom's spline, Kochanek-Bartls's spline, linear interpolation and nearest neighbor interpolation. can be applied statistical predictions - exponentially weighted moving average; - simple moving average; linear exponential weighing; - Holt's linear exponential smoothing; and an additional slowing trend. results data can be exported and sending by Internet create, delete and selection of a folder for storage data results