Time Series Analysis and prediction of bitcoin using Long Short Term Memory Neural Network

Adegboruwa, Temiloluwa I.; Adeshina, Steve A.; Boukar, Moussa Mahamat

doi:10.1109/icecco48375.2019.9043229

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…However, the work not studied for more evaluations and price projections. Similar to the proposed system, LSTM based price prediction was discussed in [8]. The author used LSTM learning for the study and proved that the system achieves less error on price predictions.…”

Section: Related Workmentioning

confidence: 99%

Crypto-Currency Price Prediction using CNN and LSTM Models

Mounika¹

2021

IJRASET

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Growing demand in internet usage has bring up to the revolution in digital economy. From the past decade digital economy is playing major role round the world. The physical assets are converted to digital assets, in which Bitcoin is the major crypto currency for the investors. Bitcoin has the biggest market capital among all other crypto currencies. Bitocoin price prediction and forecast has been one of the tedious tasks for long period. Existing works are attempted to go for accurate prediction and forecast through machine learning models. The forecast can be useful work for the investors to know about the nature of price in future and gives them directions for their investments. In this proposed work, Bitcoin price prediction is proposed through the deep learning models such as Convolutional Neural Networks (CNN) and Long short term memory (LSTM) models. The aim of the work is to give accurate predictions and forecast and bring the daily trend for crypto currency market. Experimental results shows that the proposed system given better accuracy on predictions.

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Section: Related Workmentioning

confidence: 99%

Crypto-Currency Price Prediction using CNN and LSTM Models

Mounika¹

2021

IJRASET

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“…LSTM (Adegboruwa et al., 2019) is an improved recurrent neural network (RNN), model that inherits the strong ability of RNN to process time series. Meanwhile, it also solves the problems of both gradient disappearance and gradient explosion when RNN processes long sequences.…”

Section: Architecture and Mathematical Description Of Neural Network ...mentioning

confidence: 99%

GMSK Demodulation Combining 1D‐CNN and Bi‐LSTM Network Over Strong Solar Wind Turbulence Channel

Zhu

Yang

et al. 2023

Radio Science

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During the deep space exploration of the solar system, the reliable communication link is vital, since it is the only way that enables the transmission of telecommand from the earth and the return of telemetry information and scientific data from the probe. However, the communication link in the solar system space environment faces many severe challenges. In addition, due to the ultralong communication distance, the communication link between the Earth and the solar system probe may encounter solar scintillation due to solar wind during near superior solar conjunction (Morabito & Hastrup, 2001). Solar scintillation is a telecommand and telemetry signal fluctuation phenomenon. When the transmitted telecommand and telemetry signals pass through the solar corona region, the solar wind causes telecommand and telemetry signal amplitude fluctuation, phase fluctuation and spectrum expansion. Especially, if the solar scintillation is sufficiently strong that means the heliocentric distance is less than four solar radii(Xu & Song, 2019), the communication link may be intermittently or even completely interrupted, which results in communication failure

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“…They also studied the effect of the window size prediction, the effect of using log values as inputs, the effect of data splitting, and the effect of normalization on classification and prediction problems. Then, Temiloluwa et al in [33] applied different statistical tests to the LSTM model for better prediction because statistical tests are important for time series analysis and forecasting. In [25], the author considered the Bitcoin parameters and social parameters for forecasting purposes and compared the results of the deep learning models like CNN, GRU, and LSTM, and concluded that LSTM showed the best result among all with 4-layers.…”

Section: B Deep Learning-based Predictionmentioning

confidence: 99%

Fusion in Cryptocurrency Price Prediction: A Decade Survey on Recent Advancements, Architecture, and Potential Future Directions

et al. 2022

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Cryptographic forms of money are distributed peer-to-peer (P2P) computerized exchange mediums, where the exchanges or records are secured through a protected hash set of secure hash algorithm-256 (SHA-256) and message digest 5 (MD5) calculations. Since their initiation, the prices seem highly volatile and came to their amazing cutoff points during the COVID-19 pandemic. This factor makes them a popular choice for investors with an aim to get higher returns over a short span of time. The colossal high points and low points in digital forms of money costs have drawn in analysts from the scholarly community as well as ventures to foresee their costs. A few machines and deep learning algorithms like gated recurrent unit (GRU), long short-term memory (LSTM), autoregressive integrated moving average with explanatory variable (ARIMAX), and a lot more have been utilized to exactly predict and investigate the elements influencing cryptocurrency prices. The current literature is totally centered around the forecast of digital money costs disregarding its reliance on other cryptographic forms of money. However, Dash coin is an individual cryptocurrency, but it is derived from Bitcoin and Litecoin. The change in Bitcoin and Litecoin prices affects the Dash coin price. Motivated from these, we present a cryptocurrency price prediction framework in this paper. It acknowledges different cryptographic forms of money (which are subject to one another) as information and yields higher accuracy. To illustrate this concept, we have considered a price prediction of Dash coin through the past days' prices of Dash, Litecoin, and Bitcoin as they have hierarchical dependency among them at the protocol level. We can portray the outcomes that the proposed scheme predicts the prices with low misfortune and high precision. The model can be applied to different digital money cost expectations.INDEX TERMS Cryptocurrency, price analysis, volume analysis, deep learning, machine learning, survey, ensemble model, fusion in metrics.

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Time Series Analysis and prediction of bitcoin using Long Short Term Memory Neural Network

Cited by 8 publications

References 6 publications

Crypto-Currency Price Prediction using CNN and LSTM Models

Crypto-Currency Price Prediction using CNN and LSTM Models

GMSK Demodulation Combining 1D‐CNN and Bi‐LSTM Network Over Strong Solar Wind Turbulence Channel

Fusion in Cryptocurrency Price Prediction: A Decade Survey on Recent Advancements, Architecture, and Potential Future Directions

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