Tesla stock prediction: a comparative study between four models
DOI:
https://doi.org/10.54097/y1yk0a33Keywords:
Tesla stock price prediction, linear regression, super vector regression, random forest regressor, long-short term memoryAbstract
One of the most significant components of the economy is the stock market. Due to the impact of many industries and market conditions, Tesla stock prices are continually fluctuating. Stock market forecasts are becoming more precise as artificial intelligence develops. The performance of the four methods is compared in this article, which predicts Tesla's stock using linear regression, super vector regression, RFR, and LSTM. The study's findings show that all four methodologies are capable of accurately predicting Tesla's stock prices according to four parameters (R-squared, MSE, RMSE, and MAE). Linear regression stands out among them due to its highest R-squared value (0.85) for Tesla from 2020 to 2022, compared to the other three models. The findings of this study provide empirical evidence for investors.
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