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Analysis of Machine Learning Algorithms for Time Series Prediction
Due to the rapidly increasing prominence of Artificial Intelligence in the last decade and the advancements in technology such as processing power and data storage, there has been increased interest in applying machine learning algorithms to time series prediction problems. There are many machine le...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2022
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| _version_ | 1867613206909812736 |
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| access_status_str | Open Access |
| author | Naidoo, Kimendree |
| author2 | Moodley, Deshendran |
| author_browse | Moodley, Deshendran Naidoo, Kimendree |
| author_facet | Moodley, Deshendran Naidoo, Kimendree |
| author_sort | Naidoo, Kimendree |
| collection | Thesis |
| description | Due to the rapidly increasing prominence of Artificial Intelligence in the last decade and the advancements in technology such as processing power and data storage, there has been increased interest in applying machine learning algorithms to time series prediction problems. There are many machine learning algorithms that can be used for time series prediction problems but selecting an algorithm can be challenging due to algorithms not being suitable to all types of datasets. This research investigates and evaluates machine learning algorithms that can be used for time series prediction. Experiments were carried out using the Artificial Neural Network (ANN), Support Vector Regressor (SVR) and Long Short-Term Memory (LSTM) algorithms on eight datasets. An empirical analysis was carried out by applying each machine learning algorithm to the selected datasets. A critical comparison of the algorithm performance was carried out using the Mean Absolute Error (MAE), the Mean Squared Error (MSE), the Root Mean Squared Error (RMSE) and the Mean Absolute Scaled Error (MASE). The second experiment focused on evaluating the stability and robustness of the optimal models identified in the first experiment. The key dataset characteristics identified; were the dataset size, stationarity, trend and seasonality. It was found that the LSTM performed the best for majority of the datasets, due to the algorithm's ability to deal with sequential dependency. The performance of the ANN and SVR were similar for datasets with trend and seasonality, while the LSTM overall proved superior to the aforementioned algorithms. The LSTM outperformed the ANN and SVR due to its ability to handle temporal dependency. However, due to its stochastic nature, the LSTM and ANN algorithms can have poor stability and robustness. In this regard, the LSTM was found to be a more robust algorithm than the ANN and SVR. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/36024 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:27.580Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/36024 Analysis of Machine Learning Algorithms for Time Series Prediction Naidoo, Kimendree Moodley, Deshendran Time Series Artificial Neural Network Support Vector Machine Long Short-Term Memory Due to the rapidly increasing prominence of Artificial Intelligence in the last decade and the advancements in technology such as processing power and data storage, there has been increased interest in applying machine learning algorithms to time series prediction problems. There are many machine learning algorithms that can be used for time series prediction problems but selecting an algorithm can be challenging due to algorithms not being suitable to all types of datasets. This research investigates and evaluates machine learning algorithms that can be used for time series prediction. Experiments were carried out using the Artificial Neural Network (ANN), Support Vector Regressor (SVR) and Long Short-Term Memory (LSTM) algorithms on eight datasets. An empirical analysis was carried out by applying each machine learning algorithm to the selected datasets. A critical comparison of the algorithm performance was carried out using the Mean Absolute Error (MAE), the Mean Squared Error (MSE), the Root Mean Squared Error (RMSE) and the Mean Absolute Scaled Error (MASE). The second experiment focused on evaluating the stability and robustness of the optimal models identified in the first experiment. The key dataset characteristics identified; were the dataset size, stationarity, trend and seasonality. It was found that the LSTM performed the best for majority of the datasets, due to the algorithm's ability to deal with sequential dependency. The performance of the ANN and SVR were similar for datasets with trend and seasonality, while the LSTM overall proved superior to the aforementioned algorithms. The LSTM outperformed the ANN and SVR due to its ability to handle temporal dependency. However, due to its stochastic nature, the LSTM and ANN algorithms can have poor stability and robustness. In this regard, the LSTM was found to be a more robust algorithm than the ANN and SVR. 2022-03-10T09:52:25Z 2022-03-10T09:52:25Z 2021 2022-03-08T09:55:35Z Master Thesis Masters MSc http://hdl.handle.net/11427/36024 eng application/pdf Department of Computer Science Faculty of Science |
| spellingShingle | Time Series Artificial Neural Network Support Vector Machine Long Short-Term Memory Naidoo, Kimendree Analysis of Machine Learning Algorithms for Time Series Prediction |
| thesis_degree_str | Master's |
| title | Analysis of Machine Learning Algorithms for Time Series Prediction |
| title_full | Analysis of Machine Learning Algorithms for Time Series Prediction |
| title_fullStr | Analysis of Machine Learning Algorithms for Time Series Prediction |
| title_full_unstemmed | Analysis of Machine Learning Algorithms for Time Series Prediction |
| title_short | Analysis of Machine Learning Algorithms for Time Series Prediction |
| title_sort | analysis of machine learning algorithms for time series prediction |
| topic | Time Series Artificial Neural Network Support Vector Machine Long Short-Term Memory |
| url | http://hdl.handle.net/11427/36024 |
| work_keys_str_mv | AT naidookimendree analysisofmachinelearningalgorithmsfortimeseriesprediction |