Prediction of dogecoin price using deep learning and  social media trends

Agarwal, Basant; Harjule, Priyanka; Chouhan, Lakshit; Saraswat, Upkar; Airan, Himanshu; Agarwal, Parth

doi:10.4108/eai.29-9-2021.171188

Cited by 9 publications

(6 citation statements)

References 25 publications

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“…It provides an overview of various machine learning and deep learning techniques for predicting cryptocurrency prices [5]. The literature survey highlights the growing interest in using these techniques for cryptocurrency price prediction and reviews the existing literature on the topic.…”

Section: Literature Surveymentioning

confidence: 99%

A Cryptocurrency Price Prediction Model using Deep Learning

V.,

M.V.S.,

et al. 2023

E3S Web Conf.

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Cryptocurrencies have gained immense popularity in recent years as an emerging asset class, and their prices are known to be highly volatile. Predicting cryptocurrency prices is a difficult task due to their complex nature and the absence of a central authority. In this paper, our proposal is to employ Long Short-Term Memory (LSTM) networks, a type of deep learning technique to forecast the prices of cryptocurrencies. We use historical price data and technical indicators as inputs to the LSTM model, which learns the underlying patterns and trends in the data. To improve the accuracy of the predictions, we also incorporate a Change Point Detection (CPD) technique using the Pruned Exact Linear Time (PELT) algorithm. This method allows us to detect significant changes in cryptocurrency prices and adjust the LSTM model accordingly, leading to better predictions. We evaluate our approach predominantly on Bitcoin cryptocurrency, but the model can be implemented on other cryptocurrencies provided there are valid historical price data. Our experimental results show that our proposed model outperforms the baseline LSTM algorithm, achieving higher accuracy and better performance in terms of Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). Our research findings suggest that combining deep learning techniques such as LSTM with change point detection techniques such as PELT can improve cryptocurrency price prediction accuracy and have practical implications for investors, traders, and financial analysts.

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Section: Literature Surveymentioning

confidence: 99%

A Cryptocurrency Price Prediction Model using Deep Learning

V.,

M.V.S.,

et al. 2023

E3S Web Conf.

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“…Numerous researches have been carried out to forecast time series and the value of bitcoin [10]. In contrast, deep learning models [13] and machine learning models [4] were employed to forecast the price of Bitcoin.…”

Section: Literature Reviewmentioning

confidence: 99%

“…From 2009 to 2017, the price of Bitcoin increased to over USD 20,000. As of December 2019, the daily average market volume was around USD 19.45 billion [12], and as of April 2021, the price of Bitcoin hit an all-time high of around $65000 [13]. Although investments have yielded rich returns, the constant price swings seen by most cryptocurrencies make them difficult and hazardous [14].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Cryptocurrency Price using Time Series Data and Deep Learning Algorithms

Nair,

Marie,

Abd-Elmegid

2023

IJACSA

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One of the most significant and extensively utilized cryptocurrencies is Bitcoin (BTC). It is used in many different financial and business activities. Forecasting cryptocurrency prices are crucial for investors and academics in this industry because of the frequent volatility in the price of this currency. However, because of the nonlinearity of the cryptocurrency market, it is challenging to evaluate the unique character of timeseries data, which makes it impossible to provide accurate price forecasts. Predicting cryptocurrency prices has been the subject of several research studies utilizing machine learning (ML) and deep learning (DL) based methods. This research suggests five different DL approaches. To forecast the price of the bitcoin cryptocurrency, recurrent neural networks (RNN), long shortterm memories (LSTM), gated recurrent units (GRU), bidirectional long short-term memories (Bi-LSTM), and 1D convolutional neural networks (CONV1D) were used. The experimental findings demonstrate that the LSTM outperformed RNN, GRU, Bi-LSTM, and CONV1D in terms of prediction accuracy using measures such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared score (R 2 ). With RMSE= 1978.68268, MAE=1537.14424, MSE= 3915185.15068, and R 2 = 0.94383, it may be considered the best method.

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“…The research results in sample MAPE of 9.329873 and out of sample MAPE of 3.800651. It indicates the forecasting model performance is very accurate because MAPE is less than 10% [13].…”

Section: Introductionmentioning

confidence: 95%

Comparison of ARIMA and SARIMA for Forecasting Crude Oil Prices

Ariyanti

Yusnitasari

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Crude oil price fluctuations affect the business cycle due to affecting the ups and downs of the growth of the economy, which one of the indicators of the economic business cycle phenomenon. The importance of oil price prediction requires a model that can predict future oil prices quickly, easily, and accurately so that it can be used as a reference in determining future policies. Machine learning is an accurate method that can be used in predicting and makes it easier to predict because there is no need to program computers manually. ARIMA is a machine learning algorithm while ARIMA that uses a seasonal component is called SARIMA. Based on background, research purpose is modeling crude oil price forecasting by ARIMA and SARIMA. Forecasting is done on daily crude oil price data taken from Yahoo Finance from January 27, 2020 to January 25, 2023. The evaluation results show the RMSE value of ARIMA and SARIMA is 1.905. The forecast result of 7 days ahead with ARIMA is 86.230003 while SARIMA is 86.260002. The research results are expected to be helpful for policy makers to adopt policies and make the right decisions in the use of crude oil.

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Prediction of dogecoin price using deep learning and social media trends

Cited by 9 publications

References 25 publications

A Cryptocurrency Price Prediction Model using Deep Learning

A Cryptocurrency Price Prediction Model using Deep Learning

Prediction of Cryptocurrency Price using Time Series Data and Deep Learning Algorithms

Comparison of ARIMA and SARIMA for Forecasting Crude Oil Prices

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