Nonlinear Network Time-Series Forecasting Using Redundancy Reduction

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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.