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روشی جهت پیشبینی قیمت سهام بازار بورس تهران مبتنی بر یادگیری عمیق | ||
پدافند الکترونیکی و سایبری | ||
مقاله 10، دوره 10، شماره 4 - شماره پیاپی 40، بهمن 1401، صفحه 91-100 اصل مقاله (1.28 M) | ||
نوع مقاله: مقاله پژوهشی | ||
نویسندگان | ||
طوبی ترابی پور1؛ سیده صفیه سیادت* 2 | ||
1کارشناسی ارشد، دانشگاه پیام نور، تهران، ایران | ||
2استادیار، دانشگاه پیام نور، تهران، ایران | ||
تاریخ دریافت: 15 اردیبهشت 1401، تاریخ بازنگری: 08 خرداد 1401، تاریخ پذیرش: 18 مرداد 1401 | ||
چکیده | ||
در سالهای اخیر با توجه به سوددهی بازار بورس اوراق بهادار در ایران سرمایههای خرد و کلان جذب این بازار شدند ، اما متأسفانه به دلیل دانش کم این افراد از بورس و پیشبینی قیمتها تعداد فراوانی از مردم ایران ضرر و زیان زیادی را متحمل شدند . در این تحقیق بر آن شدیم تا با استناد به تحقیق قبلی خود که از شبکه عصبی با دولایه LSTM استفاده میکرد .کار خود را قوت بخشیده و شبکه عصبی ترکیبی کانولوشن وlstm را جهت پیشبینی قیمت سهام بر روی مجموعه دیتاست وب ملت از بازار بورس اوراق بهادار تهران و سه دیتاست موجود در آن شامل آث پ ،خودرو و وساخت به کار ببریم. در انتها جهت ارزیابی روش پیشنهادی و دو روش دیگر ازنظر سه تابع خطا ،تابع میانگین مربع خطا (MSE)، تابع میانگین خطای مطلق (MAE) و تابع میانگین مربع ریشه (RMSE) بررسی شد . نتایج حاصله نشان داد در دیتاست های بزرگ با تعداد دادههای سهام بالا بسیار بهتر عمل کرده و خطای کمتری به دنبال دارد. | ||
کلیدواژهها | ||
شبکه عصبی کانولوشن؛ قیمت سهام؛ شبکه عصبی LSTM؛ یادگیری عمیق | ||
عنوان مقاله [English] | ||
A way to predict the stock price of the Tehran Stock Exchange in relation to knowledge | ||
نویسندگان [English] | ||
tuba toraby pour1؛ safieh siadat2 | ||
1Master's degree, Payam Noor University, Tehran, Iran | ||
2Assistant Professor, Payam Noor University, Tehran, Iran | ||
چکیده [English] | ||
In recent years, due to the profitability of the stock market in Iran, small and large investments were attracted to this market, but unfortunately, due to their lack of knowledge of the stock market and price forecasting, a large number of Iranians suffered great losses. In this study, we decided to use our previous research, which used a two-layer LSTM neural network, to strengthen its work and use a combination of convolution and lstm neural networks to predict stock prices on the Web Nation data set from the stock market. Use Tehran and its three databases, including ASP, car and construction. Finally, in order to evaluate the proposed method and the other two methods, three error functions, mean square error function (MSE), mean absolute error function (MAE) and root mean square function (RMSE) were evaluated. The results showed that it works much better in large datasets with high stock data and leads to fewer errors. | ||
کلیدواژهها [English] | ||
Convolution neural network, stock price, LSTM neural network, deep learning | ||
مراجع | ||
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