Nonlinear Network Time-Series Forecasting Using Redundancy Reduction

Pramod Lakshmi Narasimha, Michael T. Manry, and Changhua Yu, The University of Texas at Arlington

In this paper we propose an efficient method for forecasting highly redundant time-series based on historical information. First, redundant inputs and desired outputs are compressed and used to train a single network. Second, network output vectors are uncompressed. Our approach is successfully tested on the hourly temperature forecasting problem.

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