Stock price forecasting by ARIMA, linear regression, LSTM and decomposition linear models

Stock price forecasting has piqued the interest of academics studying finance and economics since it is a challenging but critical task for investors in financial markets. A variety of methods has been deployed, attempting to extract useful information to tackle the prediction problem. This paper presents rigorous research on the task of predicting the next-day price of Tesla stock by its past prices. A variety of methods are tested, including the long short-term memory (LSTM), the neural network model (NN), the autoregressive integrated moving average model (ARIMA), and the decomposition linear model (DL). The result is measured with mean absolute error (MAE). As shown in the experimentation result, the MAE of the ARIMA model is 7.2400, the MAE of the neural network is 5.8770, the MAE of the LSTM model is 6.8390 and the MAE of the decomposition model is 5.5890. The result suggests that the DL model performs the best in terms of MAE.