MAP-FCRNN: Multi-step ahead prediction model using forecasting correction and RNN model with memory functions

Currently, prediction stands as one of the most prominent areas of research. Enhancing the accuracy and generalization capabilities of prediction models remains a crucial and ongoing challenge. Furthermore, the majority of existing prediction models suffer from the issue of error accumulation. Thus, we develop a multi-step time series prediction model that relies on prediction correction to address this problem. First, we mitigate the problem of excessive accumulation error by constructing a sample set in combination with the prediction target. Second, we employ a recurrent neural network (RNN) model with memory function to make initial predictions. Finally, building on the concept of prediction correction, we develop a new prediction model that effectively rectifies the initial prediction results. Remarkably, the model efficiently safeguards against deviations during prediction tasks. Additionally, our proposed model integrates a clustering algorithm during the data processing phase, which introduces a rule for sample selection. This rule ensures the inclusion of diverse types of data to enhance the prediction accuracy of the model. Notably, we conduct a comparative experimental analysis using eight publicly available data sets and evaluate our model against seven commonly used prediction models to demonstrate its effectiveness.