| DC Field | Value | Language |
|---|
| dc.contributor.author | BELAMBRI, SAmy | - |
| dc.date.accessioned | 2023-10-15T08:24:25Z | - |
| dc.date.available | 2023-10-15T08:24:25Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://repository.esi-sba.dz/jspui/handle/123456789/480 | - |
| dc.description | Supervisor : Dr. Amina BELALIA / Co-Supervisor : Dr. Badia KLOUCHE | en_US |
| dc.description.abstract | Abstract :
Forecasting the price of gold on the stock market is a difficult assignment that can have
substantial repercussions for investors and experts of financial markets. The accuracy and
efficiency of financial time series forecasting has seen a revolutionary change in recent years
as a result of the incorporation of machine learning (ML), deep learning (DL), sentiment
analysis, and a wide range of other elements. This research seeks to give a thorough and
state-of-the-art examination of stock market price predictions by combining a variety of
methodologies. These methodologies include ML and DL approaches, sentiment analysis,
and external events such as pandemics, conflicts, and news.. | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Artificial Intelligence | en_US |
| dc.subject | Time Series | en_US |
| dc.subject | Technical Analysis | en_US |
| dc.subject | Financial Forecasting | en_US |
| dc.subject | LSTM | en_US |
| dc.subject | BiLSTM | en_US |
| dc.subject | CNN | en_US |
| dc.subject | Stock Market | en_US |
| dc.subject | Gold Price | en_US |
| dc.subject | Sentiment Analysis | en_US |
| dc.title | Methods for Gold Price Prediction Regarding Pandemics and News | en_US |
| dc.type | Thesis | en_US |
| Appears in Collections: | Master
|
