DC Field | Value | Language |
dc.contributor.author | BELAMBRI, SAmy | - |
dc.date.accessioned | 2023-10-15T08:29:41Z | - |
dc.date.available | 2023-10-15T08:29:41Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | https://repository.esi-sba.dz/jspui/handle/123456789/481 | - |
dc.description | Supervisor : Dr. Badia KLOUCHE / Co-Supervisor : Dr. Amina BELALIA | en_US |
dc.description.abstract | Abstract :
Predicting the stock market, and especially the price of gold, is a hard job that involves
economics, computer science, and a number of outside factors. It is a well-known subset of
artificial intelligence for researchers in finance. It gets a lot of attention in school and the
finance industry because it has so many uses and has a big effect. Researchers in machine
learning have made a lot of models and released a lot of studies in this field, most of which
are about predicting financial time series.
In the past few years, deep learning models have become strong tools that work better
than traditional machine learning methods. Building on this progress, the goal of this project
is to use deep learning techniques to make a complete model for predicting the price of gold
and then compare its accuracy to that of more standard forecasts methods. The project’s
goal is to make correct predictions about the price of gold by taking into account things like
historical data, market emotion, and outside events like pandemics, wars, and news.
To do this, methods for machine learning and deep learning will be used to look at past
data on gold prices and find patterns and trends. Also, a sentiment study will be done to find
out how the market feels and how outside factors affect gold rates. By taking these things
into account, a whole-person method to predicting the price of gold is created.
The answer that is put into place will use the latest web technologies to build an easyto-
use tool for getting to and seeing the gold price forecasts. This tool will give users useful
information and help them make smart investment choices based on predicted gold prices
and research of public opinion, pandemics, wars, and news.
In short, the goal of this project is to use machine learning, deep learning, emotion
analysis, and a number of outside factors to make an accurate and complete model for
predicting stock market gold rates. By looking at past data, market mood, and outside
events, the goal is to give buyers useful information and help them make better decisions. | 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 | Stock Market | en_US |
dc.subject | Gold Price | en_US |
dc.subject | News | en_US |
dc.subject | Covid 19 | en_US |
dc.subject | Trading | 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 | CNN | en_US |
dc.subject | BiLSTM | en_US |
dc.subject | PAXGUSDT | en_US |
dc.subject | Bert | en_US |
dc.subject | TextBlob | en_US |
dc.subject | Django | en_US |
dc.subject | Sentiment Analysis | en_US |
dc.subject | NestJs | en_US |
dc.subject | Flutter | en_US |
dc.title | Enhancing gold price forecasting using deep learning technique insights from pandemics and news data. | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Ingénieur
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