Noisy Time Series Prediction Using a Recurrent Neural Network and Grammatical Inference

@article{citeulike:2291993, abstract = {Financial forecasting is an example of a signal processing problem which is challenging due to small sample sizes, high noise, non-stationarity, and non-linearity. Neural networks have been very successful in a number of signal processing applications. We discuss fundamental limitations and inherent difficulties when using neural networks for the processing of high noise, small sample size signals. We introduce a new intelligent signal processing method which addresses the difficulties. The...}, author = {Giles, Lee C. and Lawrence, Steve and Tsoi, Ah C. }, citeulike-article-id = {2291993}, journal = {Machine Learning}, keywords = {ann, prediction, series, time}, month = {July/August}, number = {1/2}, pages = {161--183}, posted-at = {2008-03-01 04:32:32}, priority = {2}, title = {Noisy Time Series Prediction Using a Recurrent Neural Network and Grammatical Inference}, url = {http://citeseer.ist.psu.edu/giles01noisy.html}, volume = {44}, year = {2001} }

TY - JOUR ID - citeulike:2291993 L3 - citeulike-article-id:2291993 TI - Noisy Time Series Prediction Using a Recurrent Neural Network and Grammatical Inference JF - Machine Learning VL - 44 IS - 1/2 SP - 161 EP - 183 N2 - Financial forecasting is an example of a signal processing problem which is challenging due to small sample sizes, high noise, non-stationarity, and non-linearity. Neural networks have been very successful in a number of signal processing applications. We discuss fundamental limitations and inherent difficulties when using neural networks for the processing of high noise, small sample size signals. We introduce a new intelligent signal processing method which addresses the difficulties. The... KW - ann KW - prediction KW - series KW - time AU - Giles, Lee AU - Lawrence, Steve AU - Tsoi, Ah PY - 2001/July/August// UR - http://citeseer.ist.psu.edu/giles01noisy.html ER -