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A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models
Recently, deep learning techniques have been introduced and explored in the context of time series analysis and prediction.
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
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.
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.
The Performance of Machine and Deep Learning Classifiers in Detecting Zero-Day Vulnerabilities
The detection of zero-day attacks and vulnerabilities is a challenging problem.
Forecasting Economics and Financial Time Series: ARIMA vs. LSTM
Forecasting time series data is an important subject in economics, business, and finance.
