Highlights
The title is to forecast the future value of bitcoin using SVM optimised by PSO.
1. RESEARCH OBJECTIVES:
The following research objectives have been identified for this research:
1. To investigate previous research of support vector machine in financial forecasting
2. To collect sentiment-based dataset for cryptocurrencies from social media (Telegram)
3. To develop a sentiment-based support vector machine optimized by particle swarm optimization for cryptocurrency forecasting algorithms
4. To evaluate sentiment-based support vector machine for cryptocurrency forecasting
5. To compare the performance of sentiment-based support vector machine optimized by particle swarm optimization for cryptocurrency forecasting with benchmarked algorithms
2. RESEARCH QUESTIONS:
The following research questions have been developed for this research:
RQ1- What are the drawbacks or issues of support vector machine in financial forecasting?
RQ2- What is the effect of sentiment from social media towards financial forecasting (does Sentiment influence the actual price)?
RQ3- How does the support vector machine can be enhanced with the sentiment?
RQ4: Does the performance of Optimized PSO with sentiment improved the cryptocurrency forecasting compared to other ML algorithms?
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