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State space based prediction using nearest neighbors. The algorithms computes one or more nearest neighbors to an initial state vector. The images of the nearest neighbors are used to estimate to image of the initial state vector. The next iteration uses the previously computed image as new initial state vector [145].

**Syntax:**

`x = predict(pointset, length, k, stepsize, mode)`

**Input arguments:**

`pointset`- a`N`by`D`double matrix containing the coordinates of the point set, organized as`N`points of dimension`D``length`- number of iterations (length of prediction)`k`- number of nearest neighbors`stepsize`- prediction stepsize, usually one`mode`- (optional) method to estimate image of initial state vector- 0 - direct prediction, no weight is applied to neighbors
- 1 - direct prediction, biquadratic weight is applied to neighbors
- 2 - integrated prediction, no weight is applied to neighbors
- 3 - integrated prediction, biquadratic weight is applied to neighbors

**Output arguments:**

`x`- data set as double matrix, size`length`by`D`

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