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.