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In this article, we have proposed a learning to prediction based novel approach for improving the accuracy of prediction algorithms in dynamic conditions. The proposed model is composed of two modules, including the prediction module and the learning module. The learning module is responsible to regularly examine the prediction module and tune its performance by assessing its outcomes together with any other external parameters that can affect its performance. In order to determine the effectiveness of the proposed idea, a learning module based on the artificial neural network (ANN) is developed for improving the accuracy of the Kalman filter algorithm. Experimental investigations are conducted in a greenhouse indoor environment to accurately predict indoor climate parameters (temperature, CO ₂ , and humidity) from noisy sensors readings using the Kalman filter algorithm. Among the various components …