Short- and Long-Term Effective Time Series Stock Market Cost Predication Methods Using Learning Techniques
Keywords:
Stock Market Prediction, Machine Learning, Time Series Prediction, ARIMA, SARMAX, LSTM, closing cost, Opening Cost, Training, Testing MSE.Abstract
The Stock market cost is an excellent example of the time series data. The trust worthy investment options is essential for increasing the successful stick investment enhancement. Many times, investors are hesitant to decide the best stock prizes. Therefore, using machine learning (ML) might predict the stock prizes in advance. Paper presented the validation of the ML based stock market prediction for the Microsoft database. This paper considered the stock market prediction (SMP) problem as the solution via time series prediction. The continually varying nature of stock market makes it non stationary series of data this makes the cost prediction a tough problem. To increase prediction accuracy this paper proposed to examine time series auto regressive models based on optimal degree of differencing’s. The lag order is varied for achieving best production and degree of differencing’s to make model nearly stationary. The prediction modes ARIMA, SARMAX and LSTM are trained on the large Microsoft stock dataset for last 20 years. For short term prediction the ARIMA is proposed and for the long term 150 days prediction the LSTM is found to be best. Then based on the proposed model the stock cost prediction is tested using the regression model on the recent Microsoft data. The results are evaluated based on the mean square error (MSE) and absolute difference error (ADE) values for different models. Significant SMP accuracy is achieved for 150 days around 93% over rich data sample sizes.
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