Predicting the Top and Bottom Prices of Bitcoin Using Ensemble Machine Learning
سال انتشار: 1402
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 106
فایل این مقاله در 19 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_AMFA-8-3_010
تاریخ نمایه سازی: 19 تیر 1402
چکیده مقاله:
The purpose of the present study is to use the ensemble learning model to combine the predictions of Random Forest (RF), Long-Short Term Memory (LSTM), and Recurrent Neural Network (RNN) models for the Top and Bottom Prices of Bitcoin. To this aim, in the first stage, Bitcoin's top and bottom prices are predicted using three machine learning models. In the second stage, the outputs of the models are presented as feature variables to the Extreme Gradient Boosting (Xgboost) and Light Gradient Boosting Machine (LightGBM) models to predict the price tops and bottoms. Then, in the third stage, the outputs of the second stage are combined through the voting ensemble classifier pattern to predict the next top and bottom prices. The data of top and bottom Bitcoin prices in the ۱-hour time frame from ۱/۱/۲۰۱۸ to the end of ۶/۳۰/۲۰۲۲ are used as target variables and ۳۱ technical analysis indicators as feature variables for the three models in the first stage. ۷۰% of the data is regarded as learning data, and the remaining ۳۰% is considered for the second and third stages. In the second phase, ۵۰% of the data is considered for learning the output of the previous stage and ۵۰% for the test data. Finally, the prediction values are evaluated with real data for the three models and the proposed ensemble learning model. The results reveal the improvement of the performance, precision, and accuracy of the ensemble model compared to weak learning models.
کلیدواژه ها:
Algorithmic Trading ، top and bottom price prediction ، ensemble machine learning ، XGBoost ، LightGBM
نویسندگان
Emad Koosha
Financial engineering Ph.D. Candidate, Department of Financial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Mohsen Seighaly
Assistant Professor, Department of Financial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Ebrahim Abbasi
Associate Professor, Department of management, faculty of social sciences and economics, ALzahra University, Tehran, Iran
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :