The three classes of architectures for time series prediction were tested. They differ by input layers which contain either convolutional, LSTM, or dense hypercomplex layers for 4D algebras. The input was four related Stock Market time series, and the prediction of one of them is expected. The optimization of hyperparameters related to the classes of architectures was performed in order to compare the best neural networks within the class. The results show that in most cases, the architecture with a hypercomplex dense layer provides similar MAE accuracy to other architectures, however, with considerably less trainable parameters. Thanks to it, hypercomplex neural networks can be learned and process data faster than the other tested architectures. Moreover, the order of the input time series has an impact on effectively.

翻译：为了比较时间序列预测的三种架构类别，我们对其进行了测试。这些架构的输入层分别包含用于四维代数的卷积层、LSTM层或密集超复数层。输入数据为四个相关的股票市场时间序列，并预期对其中之一进行预测。通过优化与架构类别相关的超参数，以比较同类中的最优神经网络。结果表明，在大多数情况下，采用超复数密集层的架构在平均绝对误差（MAE）精度上与其他架构相当，但可训练参数显著减少。得益于此，超复数神经网络相比其他测试架构能更快完成学习与数据处理。此外，输入时间序列的顺序对预测有效性具有影响。