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A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM
The major question is that whether the gates incorporated in the LSTM architecture already offers a good prediction and whether additional training of data would be necessary to further improve the prediction.
Locality Sensitive Hashing-based Sequence Alignment Using Deep Bidirectional LSTM Models
These sequences are then fed into the bidirectional LSTM model and then mapped into fixed-length vectors.
Clustering Time Series Data through Autoencoder-based Deep Learning Models
In particular, deep learning techniques are capable of capturing and learning hidden features in a given data sets and thus building a more accurate prediction model for clustering and labeling problem.
Fast Fourier Transformation for Optimizing Convolutional Neural Networks in Object Recognition
Our model demonstrated improvement in training time during convolution from $600-700$ ms/step to $400-500$ ms/step.
A Concern Analysis of FOMC Statements Comparing The Great Recession and The COVID-19 Pandemic
It is important and informative to compare and contrast major economic crises in order to confront novel and unknown cases such as the COVID-19 pandemic.
Computational Engineering, Finance, and Science General Economics Economics
