Reinforced Two-Step-Ahead Weight Adjustment Technique for Online Training of Recurrent Neural Networks


A reliable forecast of future events possesses nice value. The main purpose of this paper is to propose an innovative learning technique for reinforcing the accuracy of 2-step-ahead (2SA) forecasts. The real-time recurrent learning (RTRL) algorithm for recurrent neural networks (RNNs) will effectively model the dynamics of complex processes and has been used successfully in one-step-ahead forecasts for various time series. A bolstered RTRL algorithm for 2SA forecasts using RNNs is proposed during this paper, and its performance is investigated by two famous benchmark time series and a streamflow throughout flood events in Taiwan. Results demonstrate that the proposed bolstered 2SA RTRL algorithm for RNNs can adequately forecast the benchmark (theoretical) time series, considerably improve the accuracy of flood forecasts, and effectively scale back time-lag effects.

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