Stock market prediction using time series analysis
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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BRAC University
2019
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| Online pristup: | http://hdl.handle.net/10361/11427 |
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10361-11427 |
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10361-114272022-01-26T10:08:26Z Stock market prediction using time series analysis Hira, Farhan Islam Maruf, Mazharul Ferdous Hossain, Afzal Arif, Hossain Department of Computer Science and Engineering, BRAC University Stock market Time series analysis Prediction statistical mechanics type models Statistical Theory and Methods. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Includes bibliographical references (page 40). Cataloged from PDF version of thesis. Stock market, a very unpredictable sector of finance, involves a large number of investors, buyers and sellers. Stock prediction has been a phenomenon since machine learning was introduced. But very few techniques became useful for forecasting the stock market as it changes with the passage of time. As time is playing a crucial rule here, Time Series (TS) analysis is used in this paper to predict short-term stock market. The first step for analyzing TS is to check whether historical stock market data is stationary using Plotting Rolling Statistics and Dickey-Fuller Test. Secondly, Trend and Seasonality is eliminated from the series to make the data a stationary series. Then, TS stochastic model known as Autoregressive Integrated Moving Average (ARIMA) is used as it has been broadly applied in financial and economic sectors for its efficiency and great potentiality for short-term stock market prediction. For comparing the performance, the three subclasses of ARIMA such as: Autoregressive (AR), Moving Average (MA), and Autoregressive Moving Average (ARMA) are also applied. Finally, the forecasted values are converted to the original scale by applying Trend and Seasonality constraints back. KEYWORDS: Stock Prediction, Machine Learning, Time Series, ARMA, ARIMA. Farhan Islam Hira Mazharul Ferdous Maruf Afzal Hossain B. Computer Science and Engineering 2019-02-18T04:43:16Z 2019-02-18T04:43:16Z 2018 2018-12 Thesis ID 14301014 ID 14101228 ID 14101187 http://hdl.handle.net/10361/11427 en BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 40 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Stock market Time series analysis Prediction statistical mechanics type models Statistical Theory and Methods. |
| spellingShingle |
Stock market Time series analysis Prediction statistical mechanics type models Statistical Theory and Methods. Hira, Farhan Islam Maruf, Mazharul Ferdous Hossain, Afzal Stock market prediction using time series analysis |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Arif, Hossain |
| author_facet |
Arif, Hossain Hira, Farhan Islam Maruf, Mazharul Ferdous Hossain, Afzal |
| format |
Thesis |
| author |
Hira, Farhan Islam Maruf, Mazharul Ferdous Hossain, Afzal |
| author_sort |
Hira, Farhan Islam |
| title |
Stock market prediction using time series analysis |
| title_short |
Stock market prediction using time series analysis |
| title_full |
Stock market prediction using time series analysis |
| title_fullStr |
Stock market prediction using time series analysis |
| title_full_unstemmed |
Stock market prediction using time series analysis |
| title_sort |
stock market prediction using time series analysis |
| publisher |
BRAC University |
| publishDate |
2019 |
| url |
http://hdl.handle.net/10361/11427 |
| work_keys_str_mv |
AT hirafarhanislam stockmarketpredictionusingtimeseriesanalysis AT marufmazharulferdous stockmarketpredictionusingtimeseriesanalysis AT hossainafzal stockmarketpredictionusingtimeseriesanalysis |
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1814307543882661888 |