Hypercomplex neural network in time series forecasting of stock data
CoRR(2024)
Abstract
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.
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