  The paper introduces a novel topological method for prediction and modeling
for a nonlinear time--series that exhibit recurring patterns. According to the
model, global manifold of the reconstructed state--space can be approximated by
a few overlapping recurrence neighborhoods. The inherent redundancy structure
of the delay embedding procedure and the property of recurrence are used to
reduce the computational load, which is inevitable in nonlinear analysis. The
modeling and prediction possibilities of the model are demonstrated using (i) a
numerical data generated by a dynamical system: the Duffing oscillator and (ii)
a real--life data: Electrocardiogram ECG data of a healthy human. A potential
application of the proposed model is demonstrated for a multivariate
cardiovascular data set that exhibits the property of recurrence. Real--time
monitoring of cardiovascular signals are essential in clinical research and
corruption of data are very common. It is a challenging task for a model to
perform cognitive functions based on the contextual information, explicitly
predicting gaps or loss of data and identifying noises in the physiological
data. Paper concludes with an application of the proposed model in addressing
some of these the issues.
