  The goal of this paper is to test three classes of neural network (NN)
architectures based on four-dimensional (4D) hypercomplex algebras for time
series prediction. We evaluate different architectures, varying the input
layers to include convolutional, Long Short-Term Memory (LSTM), or dense
hypercomplex layers for 4D algebras. Four related Stock Market time series are
used as input data, with the prediction focused on one of them. Hyperparameter
optimization for each architecture class was conducted to compare the
best-performing neural networks within each class. The results indicate that,
in most cases, architectures with hypercomplex dense layers achieve similar
Mean Absolute Error (MAE) accuracy compared to other architectures, but with
significantly fewer trainable parameters. Consequently, hypercomplex neural
networks demonstrate the ability to learn and process time series data faster
than the other tested architectures. Additionally, it was found that the
ordering of the input time series have a notable impact on effectiveness.
