DLCP2F: a DL-based cryptocurrency price prediction framework

نویسندگان

چکیده

Abstract Cryptocurrencies are distributed digital currencies that have emerged as a consequence of financial technology advancement. In 2017, cryptocurrencies shown huge rise in their market capitalization and popularity. They now employed today’s systems individual investors, corporate firms, big institutions heavily investing them. However, this industry is less stable than traditional currency markets. It can be affected by several legal, sentimental, technical factors, so it highly volatile, dynamic, uncertain, unpredictable, hence, accurate forecasting essential. Recently, cryptocurrency price prediction becomes trending research topic globally. Various machine deep learning algorithms, e.g., Neural Networks (NN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) were utilized to analyze the factors influencing prices accordingly predict This paper suggests five-phase framework for based on two state-of-the-art architectures (i.e., BiLSTM GRU). The current study uses three public real-time datasets from “Yahoo Finance”. learning-based algorithms used forecast popular Bitcoin, Ethereum, Cardano). Grid Search approach hyperparameters optimization processes. Results indicate GRU outperformed algorithm Cardano, respectively. lowest RMSE model was found 0.01711, 0.02662, 0.00852 Experimental results proved significant performance proposed achieves minimum MSE values.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Cryptocurrency Price Prediction Using News and Social Media Sentiment

This project analyzes the ability of news and social media data to predict price fluctuations for three cryptocurrencies: bitcoin, litecoin and ethereum. Traditional supervised learning algorithms were utilized for text-based sentiment classification, but with a twist. Daily news and social media data was labeled based on actual price changes one day in the future for each coin, rather than on ...

متن کامل

Extending the 5S Digital Library (DL) Framework: From a Minimal DL towards a DL Reference Model

In this paper, we describe ongoing research in three DL projects that build upon a common foundation – the 5S DL framework. In each project, we extend the 5S framework to provide specifications for a particular type of DL service and/or system – finally, moving towards a DL reference model. In the first project, we are working on formalizing content-based image retrieval services in a DL. In th...

متن کامل

Ensemble Neurocomputing Based Oil Price Prediction

In this paper, we investigated an ensemble neural network for the prediction of oil prices. Daily data from 1999 to 2012 were used to predict the West Taxes, Intermediate. Data were separated into four phases of training and testing using different percentages and obtained seven sub-datasets after implementing different attribute selection algorithms. We used three types of neural networks: Fee...

متن کامل

Price Prediction Strategies for Market-Based Scheduling

In a market-based scheduling mechanism, the allocation of time-specific resources to tasks is governed by a competitive bidding process. Agents bidding for multiple, separately allocated time slots face the risk that they will succeed in obtaining only part of their requirement, incurring expenses for potentially worthless slots. We investigate the use of price prediction strategies to manage s...

متن کامل

Implementation of a Prediction-Based Cognitive Framework

Predictive analysis in many business domains is hampered by the massive quantities of information that must be analyzed. Given the relative strength of computers at processing large volumes of data, increasing the predictive powers of machines is an important goal. This paper describes a framework for human cognition that is based on empirical evidence for the role of prediction in cognition, a...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Discover Artificial Intelligence

سال: 2022

ISSN: ['2731-0809']

DOI: https://doi.org/10.1007/s44163-022-00036-2