Forecasting of telecommunication stock prices using machine learning algorithms
Not available
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| Format: | Master's thesis |
| Language: | English |
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Universiti Teknologi Malaysia
2025
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| Online Access: | https://utmik.utm.my/handle/123456789/40419 |
| Abstract | Abstract here |
| _version_ | 1854975039146819584 |
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| author | Fong, Lee Ling |
| author2 | Nor Azizah Ali, supervisor |
| author_facet | Nor Azizah Ali, supervisor Fong, Lee Ling |
| author_sort | Fong, Lee Ling |
| description | Not available |
| format | Master's thesis |
| id | utm-123456789-40419 |
| institution | Universiti Teknologi Malaysia |
| language | English |
| publishDate | 2025 |
| publisher | Universiti Teknologi Malaysia |
| record_format | dspace |
| record_pdf | Abstract |
| spelling | utm-123456789-404192025-08-20T15:36:19Z Forecasting of telecommunication stock prices using machine learning algorithms Fong, Lee Ling Nor Azizah Ali, supervisor Computing Not available Forecasting is a process that produces accurate predictions of the future course of trends using past data as inputs. The stock exchanges are marketplaces where stockbrokers and traders may purchase and sell securities including stocks, bonds, and other financial instruments. Forecasting of stock prices is very important in analyst the trend of stock prices since forecasting of stock market seeks to foresee future variations in a commercial exchange's stock value. However, forecasting of stock prices still one of the most challenging tasks due to obvious various variables involved in stock forecasting, which lead to the stock market highly uncertainty. In order to forecast the stock prices, machine learning algorithms can be introduced to forecast the trend of stock value. There are numerous methods have been used by other researchers to implement the forecasting of stock prices previously. The purpose of this study is to investigate the application of machine learning algorithms and statistical time series forecasting methods in modelling the financial time series data. The models implemented including Decision Tree Regression, Random Forest Regression, Polynomial Regression, K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Autoregressive Integrated Moving Average (ARIMA), and Exponential Smoothing (ETS). The benchmark that used for comparison in this study is the previous study that done by Mambu et al. (2020). The best model has been proposed as forecasting model after comparison in this study. In addition, dashboards have been developed by using Tableau Desktop for visualization in this study. The best model obtained for the EXCL and ISAT dataset is the Polynomial Regression model with RMSE 0.0008 and 0.0036 respectively. The best model obtained for the FREN dataset is the Artificial Neural Network (ANN) model with RMSE 0.0863. The best model obtained for the TLKM dataset is the Random Forest model with RMSE 0.0029. shafika UTM 125 p. Thesis (Master of Science (Data Science)) - Universiti Teknologi Malaysia, 2023 2025-03-06T09:57:43Z 2025-03-06T09:57:43Z 2023 Master's thesis https://utmik.utm.my/handle/123456789/40419 vital:152845 valet-20230820-083959 ENG Closed Access UTM Complete Unpublished Completion application/pdf Universiti Teknologi Malaysia |
| spellingShingle | Computing Fong, Lee Ling Forecasting of telecommunication stock prices using machine learning algorithms |
| thesis_level | Master |
| title | Forecasting of telecommunication stock prices using machine learning algorithms |
| title_full | Forecasting of telecommunication stock prices using machine learning algorithms |
| title_fullStr | Forecasting of telecommunication stock prices using machine learning algorithms |
| title_full_unstemmed | Forecasting of telecommunication stock prices using machine learning algorithms |
| title_short | Forecasting of telecommunication stock prices using machine learning algorithms |
| title_sort | forecasting of telecommunication stock prices using machine learning algorithms |
| topic | Computing |
| url | https://utmik.utm.my/handle/123456789/40419 |
| work_keys_str_mv | AT fongleeling forecastingoftelecommunicationstockpricesusingmachinelearningalgorithms |