Improving the performance of Recurrent Neural Networks for time series prediction by combining Long Short-Term Memory and Attention Long Short-Term Memory

Recurrent neural networks (RNNs) used in time series prediction are still not perfect in their predictions and improvements can still be made in the area. Most recently transformers have led to great improvements in the field of RNNs, however transformers can not be used on time series data, because the architecture of transformers does not...

Attention LSTM : Application in Stock Price Prediction on Single Companies

According to the development of data-related techniques, aimed at exploring the largest value of data, price prediction has been seen as more vital for quantifying and pricing stock. To solve this problem, the learning based algorithm became popular during modern computing techniques development. LSTM (Long Short-Term Memory) techniques attract...