Analysis of financial data on the time series using data from the stock market

Analysis of financial data on the time series using data from the stock market

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.

Bibliografiske detaljer
Main Authors:	Shachcha, Ifad Bhuiyan, Siam, Muhammad Ziaus
Andre forfattere:	Rasel, Annajiat Alim
Format:	Thesis
Sprog:	en_US
Udgivet:	Brac University 2022
Fag:	Stock market Machine Learning Finance Prediction Dense NN RNN LSTM CNN Business enterprises > Finance.
Online adgang:	http://hdl.handle.net/10361/17645

Lignende værker

Stock Market Price movement prediction using RNN and Point-weight Sentiment
af: Uddin, S. M. Rageeb Noor, et al.
Udgivet: (2022)

Human sentiment analysis using natural language processing
af: Shamya, Md. Shamiul Haque Khan, et al.
Udgivet: (2024)

Stock price prediction using time series data
af: Mazed, Mashtura
Udgivet: (2019)

RED-LSTM: Real time emotion detection using LSTM
af: Labiba, Mansura Rahman, et al.
Udgivet: (2023)

Predicting crime using deep learning
af: Shihab, Muhammad Nafees, et al.
Udgivet: (2024)

Bangla speech recognition using 1D CNN and LSTM with different dimension reduction techniques
af: Nirjhor, S M Mahsanul Islam, et al.
Udgivet: (2019)

Stock price prediction
af: Das, Kaushik, et al.
Udgivet: (2023)

Stock market prediction using time series analysis
af: Hira, Farhan Islam, et al.
Udgivet: (2019)

Comparative analysis of machine learning models for stock price analysis across different dataset sizes
af: Srabon, Abir Alam, et al.
Udgivet: (2023)

Dhaka Stock Market analysis with ARIMA-LSTM Hybrid Model
af: Arnob, Raisul Islam, et al.
Udgivet: (2020)

Forecasting stock market prices using advanced tools of machine learning
af: Anwar, Md. Tawhid, et al.
Udgivet: (2019)

A computer vision based approach for stalking detection using CNN-LSTM hybrid model
af: Iqbal, Shahriar, et al.
Udgivet: (2023)

A deep learning approach towards soft biometrics attributes prediction using CNN
af: Kibria, Maharab, et al.
Udgivet: (2022)

Predicting effectiveness of marketing through analyzing emotional context in advertisement using deep learning
af: Arafat, Sheikh Mohammad, et al.
Udgivet: (2021)

A deep learning approach to predict crypto-currency price by evaluating sentiment and stock market correlations
af: Maliha, Miftahul Zannat, et al.
Udgivet: (2023)

Child Safe Browser Extension: A Browser Extension to Detect Adultery and Violent Content to Make Safer Web for Children
af: Alhossen, Md., et al.
Udgivet: (2021)

Stock market prediction using ensemble learning
af: Rabbani, Rakin Bin, et al.
Udgivet: (2021)

Analyzing area-wise air pollution level using machine learning for a better future
af: Sihan, Sk. Atik Tajwar, et al.
Udgivet: (2021)

Cryptocurrency price prediction and forecasting using machine learning algorithm and long- short term memory mapping
af: Zaman, Md. Sayeed Ibne, et al.
Udgivet: (2022)

Dhaka Stock Exchange stock price prediction using Machine Learning and Deep Learning Models
af: Barua, Debalina
Udgivet: (2023)

Forecasting Dhaka stock exchange prices using machine learning models: a Performance analysis
af: Alam, Md. Iftekharul, et al.
Udgivet: (2024)

Performance analysis of deep learning algorithms for Intrusion Detection System(IDS) of IoT Security
af: Fiha, Yeamin Jahan, et al.
Udgivet: (2022)

Hybrid recommendation system of intelligent captioning using deep learning networks
af: Wasi, Ahamed Al, et al.
Udgivet: (2024)

Recognition of Bangladeshi sign language from 2D videos using openpose and LSTM based RNN
af: Dewanjee, Tanmoy, et al.
Udgivet: (2021)

Automated image caption generator in Bangla using multimodal learning
af: Rodoshi, Mashiat Hasin, et al.
Udgivet: (2023)

Demand forecasting on supply chain using ML and NN
af: Hridi, Naoshin Anzum, et al.
Udgivet: (2023)

Data analysis on the Bangladesh share market using Machine-Learning
af: Sadman, Syed Hassan, et al.
Udgivet: (2021)

Classification of Bangladeshi soil texture using convolutional neural network
af: Raj, Hafiz Mohiuddin, et al.
Udgivet: (2023)

Bangla hate speech detection on social media using attention-based recurrent neural network
af: Das, Amit Kumar, et al.
Udgivet: (2022)

The global stock market
af: Wójcik, Dariusz

Potential of the stock trade of Bangladesh: An overview of BRAC EPL stock brokerage
af: Binte Zahid, Jannat
Udgivet: (2023)

Common stocks and uncommon profits and other writings by Philip A. Fisher /
af: Fisher, Philip A.
Udgivet: (1996)

Anomalous behavior detection using Spatio temporal Feature and 3D CNN model for Surveillance
af: Nahar, Jannatun, et al.
Udgivet: (2022)

Implementing machine learning techniques to forecast floods In Bangladesh based on historical data
af: Toufique, S.M, et al.
Udgivet: (2024)

A hybrid deep learning model and explainable AI-based Bengali hate speech multi-label classification and interpretation
af: Shakil, Mahmudul Hasan
Udgivet: (2023)

Sound classification using deep learning for hard of hearing and deaf people
af: Habib, Md.Adnan, et al.
Udgivet: (2022)

Prediction of coronary heart diseases using supervised machine learning algorithms
af: Salam, Nazia Binte, et al.
Udgivet: (2023)

An improved deepfake detection using deep learning
af: Paul, Annay, et al.
Udgivet: (2023)

Market efficiency in weak form: empirical evidence from Bangladesh Stock Market
af: Hossain, Md. Delowar
Udgivet: (2019)

Voice impersonation detection using LSTM based RNN and explainable AI
af: Barua, Kawshik, et al.
Udgivet: (2022)