مطالب مرتبط با کلیدواژه

Time Series Prediction


۱.

Support Vector Regression Parameters Optimization using Golden Sine Algorithm and Its Application in Stock Market(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۷, Issue ۲, Spring ۲۰۲۲ 477-487

کلیدواژه‌ها: Golden Sine Algorithm Meta-heuristics Optimization Algorithms Parameter Tuning Support vector regression Time Series Prediction

حوزه های تخصصی:
تعداد بازدید : ۳۵۳ تعداد دانلود : ۱۵۴
Stock price prediction is one of the most important concerns of stockholders. This prediction, independent of the method which is used or the assumptions which are applied, is welcomed and trusted if it can guarantee a high fitting. So due to the high performance prediction, using some complicated models as Machine Learning family such as Support Vector Regression (SVR) was recommended instead of older and lower performance approaches such as multiple discriminant technique. SVR model have achieved high performance on forecasting problems, however, its performance is highly dependent on the appropriate selection of SVR parameters. In this study, a novel GSA-SVR model based on Golden Sine Algorithm is presented. The performance of the proposed model is compared with eleven other meta-heuristic algorithms on some stocks from NASDAQ. The results indicate that the given model here is capable of optimizing the SVR parameters very well and indeed is one of the best models judged by both prediction performance accuracy and time consumption.
۲.

Stock Price Forecasting in Iran Stock Market: A Comparative Analysis of Deep-learning Approaches(مقاله علمی وزارت علوم)

نویسنده:
منبع: International Journal of Web Research, Volume ۶, Issue ۲,Autumn-Winter ۲۰۲۳ 29 - 42
تعداد بازدید : ۳۲ تعداد دانلود : ۱۹
The capital market plays a crucial role within a country's financial structure and is instrumental in funding significant, long-term projects. Investments in the railway transport industry are vital for boosting other economic areas and have a profound impact on macroeconomic dynamics. Nonetheless, the potential for delayed or uncertain returns may deter investors. Accurate predictions of rail company stock prices on exchanges are therefore vital for making informed investment choices and securing sustained investment. This study employs deep learning techniques to forecast the closing prices of MAPNA and Toucaril shares on the Tehran Stock Exchange. It utilizes deep neural networks, specifically One-dimensional Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM) networks, and a combined CNN-LSTM model, for stock price prediction. The effectiveness of these models is measured using various metrics, including MAE, MSE, RMSE, MAPE, and R2. Findings indicate that deep learning methods can predict stock prices effectively, with the CNN-LSTM model outperforming others in this research. According to the results, The CNN-LSTM model reached the highest R2 of 0.992. Also, based on criteria such as MAE, MSE, RMSE, and MAPE the best results belong to LSTM (Kaggle-modified) with 521.715, 651119.194, 806.920, and 0.028, respectively.