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In the past decade, deep learning models have shown to be promising tools for time series forecasting. However, owing to significant differences in the volatility characteristics among different types of time series data, it is difficult for an individual deep learning model to maintain robust forecasting performance. In this study, a novel ensemble deep learning model is proposed to achieve accurate and stable time series forecasting. First, a boosting deep learning method based on extended AdaBoost algorithm is proposed for generating various basic predictors. These basic predictors are further enhanced through a new dynamic error correction method. A stacking-based ensemble method that employs kernel ridge regression as the meta-predictor is then used to combine the basic predictors to produce the ultimate forecasting results. To increase forecasting accuracy and stability, an enhanced multi-population non-dominated sorting genetic algorithm-II is proposed for ensemble pruning. Finally, the forecasting performance of the proposed model is verified through the use of three different types of real-world time series data (i.e., PM 2.5 concentration, wind speed, and electricity price). The experimental results showed that the proposed model is superior to other baseline models in dealing with time series forecasting tasks. [ABSTRACT FROM AUTHOR]

This paper introduces a new quaternion multi-valued neural network architecture and demonstrates its potential with numerical examples of multi-channel prediction and classification. A variety of real-valued learning structures have been introduced in prior literature; an important example is the multilayer perceptron neural network, which forms the underlying basis for modern deep learning architectures. However, in multidimensional information processing problems, real-valued learning structures perform suboptimally due to distortion of inter-channel relationships. A natural way to represent multidimensional data is using quaternions, a four-dimensional associative normed division algebra over the real numbers that allows for the multiplication and division of points in three-dimensional space. This paper introduces quaternion multi-valued neural networks, which perform nonlinear operations on the three-dimensional phase of quaternion data points. As shown with two numerical examples, the proposed quaternion multi-valued neural network outperforms existing learning structures, particularly in cases where limited training data is available. [ABSTRACT FROM AUTHOR]