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.

对三种用于时间序列预测的架构进行了测试，它们分别包含卷积层、LSTM 层和密集超复数层用于 4D 代数。通过优化与架构类别相关的超参数，比较了最佳神经网络在类别内的性能。结果显示，在大多数情况下，具有超复数密集层的架构与其他架构相比，提供了类似的 MAE 准确性，但可训练参数较少。由于这一特点，超复数神经网络可以比其他测试的架构更快地学习和处理数据。此外，输入时间序列的顺序对有效性产生影响。