TY - GEN
T1 - Measures of Serial Data Compressibility by Neural Network Predictors
AU - Coughlin, James P.
AU - Baran, R. H.
AU - Ko, Hanseok
N1 - Publisher Copyright:
© 1992 IEEE.
PY - 1992
Y1 - 1992
N2 - A time series or univariate random process is compressible if it is predictable. Experiments with a variety of processes readily show that adaptive neural networks are at least as effective as their linear counterparts in one-step-ahead prediction. We explore the relationship between the predictive accuracy attained by the network, in the long run, and the closeness with which it can fit (and overfit) small segments of the same series in the course of many passes through the same data. Our findings suggest that the predictability of a process can be estimated by measuring the ease with which its increments can be overfitted.
AB - A time series or univariate random process is compressible if it is predictable. Experiments with a variety of processes readily show that adaptive neural networks are at least as effective as their linear counterparts in one-step-ahead prediction. We explore the relationship between the predictive accuracy attained by the network, in the long run, and the closeness with which it can fit (and overfit) small segments of the same series in the course of many passes through the same data. Our findings suggest that the predictability of a process can be estimated by measuring the ease with which its increments can be overfitted.
UR - http://www.scopus.com/inward/record.url?scp=85132006007&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.1992.287096
DO - 10.1109/IJCNN.1992.287096
M3 - Conference contribution
AN - SCOPUS:85132006007
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 755
EP - 760
BT - Proceedings - 1992 International Joint Conference on Neural Networks, IJCNN 1992
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1992 International Joint Conference on Neural Networks, IJCNN 1992
Y2 - 7 June 1992 through 11 June 1992
ER -