کاهش درصد خطای پیشبینی سریهای زمانی قیمت رمزارزها با استفاده از دوسویهسازی شبکههای عصبی یادگیری عمیق
الموضوعات : electrical and computer engineeringفتانه کاظم زاده 1 , مسعود هوشمند کفاشیان 2 , منیره هوشمند 3
1 - دانشگاه بین المللی امام رضا (ع)
2 - شرکت مخابرات، مشهد
3 - دانشگاه بین المللی امام رضا (ع)
الکلمات المفتاحية: پیشبینی سریهای زمانی, یادگیری عمیق, شبکه عصبی دوطرفه, پیشبینی قیمت رمزارزها, خطای پیشبینی شبکه عصبی, درصد خطای پیشبینی قیمت.,
ملخص المقالة :
پیشبینی سریهای زمانی در حوزههای مهندسی، مخابرات و امور مالی از اهمیت بالایی برخوردار است. سریهای زمانی مالی، که اغلب چند متغیره هستند، نیاز به الگوریتمهای دقیق و بهینه دارند. در پژوهشهای سالهای اخیر، شبکههای عصبی عمیق در بهبود دقت پیشبینی سریهای زمانی مالی نتایج موفقی نشان دادهاند. این پژوهش به بررسی استفاده از شبکههای LSTM و GRU در پیشبینی قیمت رمزارزها پرداخته و رویکرد دوسویهسازی این شبکهها را با تاکید به انتخاب بهینه هایپرپارامترها به منظور کاهش خطای پیشبینی و افزایش دقت با استفاده از روشهای جستجوی شبکهای، جستجوی اتفاقیCV و بیزین مورد مطالعه قرار میدهد. نتایج شبیهسازی نشان میدهند که استفاده از شبکههای دو سویه LSTM و شبکه دوسویه BiGRU کاهش درصد خطا را برای رمزارز BTC تا 22/3% ، برای ETH تا 94/3% ، و برای LTC تا 99/3% به همراه داشته است.
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