1. A system for temporal prediction, comprising one or more processors having: an extraction module, the extraction module being configured to receive X(1),. .. , X(n) historical samples of a time series and utilize a search and optimization algorithm to extract deterministic features in the time series, wherein the deterministic features are phase-space representations (PSR) of the time series; a mapping module, the mapping module being configured to receive the deterministic features and utilize a learning algorithm to map the deterministic features to a predicted {circumflex over (x)}(n+1) sample of the time series; and a prediction module, the prediction module being configured to utilize a cascaded computing structure having k levels of prediction to generate a predicted {circumflex over (x)}(n+k) sample, the predicted {circumflex over (x)}(n+k) sample being a final temporal prediction for k future samples, wherein the prediction module is configured to utilize a cascaded computing structure having k levels of prediction, wherein each level of prediction is configured to receive the X(1) through X(n) historical samples and the past {circumflex over (x)}(n+1) sample through a {circumflex over (x)}(n+k−1) sample, and wherein the prediction module further utilizes the extraction module and mapping module to generate a predicted {circumflex over (x)}(n+k) sample, the predicted {circumflex over (x)}(n+k) sample being a final temporal prediction for k future samples; and wherein {circumflex over (x)}(n+k)=G(P n+k−1 ) and P n+k−1 ={w i x(n+k−1−d i )} m i=1 , where w i is a weight factor, d i is a delay factor, and m is an embedded dimension, with parameters {w i ,d i ,m} being independent of prediction horizon.