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Chahine, Firas (2023) Comparative Analysis of ARIMA and LSTM Models for Predicting Electricity Consumption, Electricity Price and Stock Prices: A Case Study of Victoria, Australia. Masters thesis, Dublin, National College of Ireland.
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Abstract
In an era when data-driven decision-making is becoming increasingly important, accurate prediction of complex events is critical. This research focuses on the predictive analytics phenomena by conducting a comparative analysis between the implementation of ARIMA and LSTM model on the S&P/ASX200 index on one hand, and the price and demand on the electricity in Victoria Australia on the other hand. The study focuses on the task of predicting the stock price and the demand on the electricity of Victoria, as well as the future stock market dynamics. The study is to investigate the usefulness of these techniques in forecasting both by using historical datasets encompassing on the stock prices and the power consumption data. The study findings provide useful insights that might possibly improve strategic decision-making in the energy and finance sectors, as well as showing strength and weaknesses of both algorithms methodologies used.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Byrne, Brian UNSPECIFIED |
| Subjects: | T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electricity Supply H Social Sciences > HG Finance > Fintech T Technology > T Technology (General) > Information Technology > Fintech H Social Sciences > HG Finance > Investment > Stock Exchange |
| Divisions: | School of Computing > Master of Science in FinTech |
| Depositing User: | Tamara Malone |
| Date Deposited: | 02 Aug 2024 10:35 |
| Last Modified: | 02 Aug 2024 10:35 |
| URI: | https://norma.ncirl.ie/id/eprint/7014 |
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