Improved stochastic configuration network ensemble methods for time-series forecasting

Stochastic configuration network ensemble (SCNE) learning is an effective method for handling large datasets and reducing the number of input window size adjustments. However, this model faces challenges in effectively capturing the temporal features of time-series datasets and is constrained by certain hyperparameters, leading to longer training times. To address these issues, this paper proposes an improved stochastic configuration network ensemble (ISCNE) algorithm. Firstly, this algorithm retains the end data of the original dataset during the dataset partitioning process, effectively reducing prediction errors. Secondly, the direct ensemble algorithm eliminates redundant hyperparameters in SCNE, simplifying the model construction process. Lastly, the use of an improved normal distribution sampling strategy for input weights and biases, which provides values close to the mean and relatively small, reduces errors and further shortens the training time. The method was tested on 8 benchmark time-series forecasting datasets. Experimental results indicate that, it effectively improves the accuracy of time-series forecasting and model training efficiency while having a more streamlined structure. •Improved stochastic configuration ensemble for time series forecasting.•Dynamically construct the network structure.•A novel sub-dataset partitioning method for time series forecasting is proposed.•A simple and efficient ensemble method.•Enhanced normal distribution sampling to improve training accuracy.