Prediction of Cryptocurrency Price using Time Series Data and Deep Learning Algorithms Article Swipe
M. Thamban Nair
,
Mohamed I. Marie
,
Laila A. Abd-Elmegid
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2023.0140837
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2023.0140837
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 time-series 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 short-term 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 (R2). With RMSE= 1978.68268, MAE=1537.14424, MSE= 3915185.15068, and R2= 0.94383, it may be considered the best method.
Related Topics
Concepts
Cryptocurrency
Mean squared error
Computer science
Artificial intelligence
Recurrent neural network
Machine learning
Mean absolute error
Time series
Deep learning
Volatility (finance)
Series (stratigraphy)
Artificial neural network
Convolutional neural network
Algorithm
Econometrics
Statistics
Mathematics
Biology
Computer security
Paleontology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14569/ijacsa.2023.0140837
- http://thesai.org/Downloads/Volume14No8/Paper_37-Prediction_of_Cryptocurrency_Price_using_Time_Series_Data.pdf
- OA Status
- diamond
- Cited By
- 13
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386394567
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386394567Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.14569/ijacsa.2023.0140837Digital Object Identifier
- Title
-
Prediction of Cryptocurrency Price using Time Series Data and Deep Learning AlgorithmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
M. Thamban Nair, Mohamed I. Marie, Laila A. Abd-ElmegidList of authors in order
- Landing page
-
https://doi.org/10.14569/ijacsa.2023.0140837Publisher landing page
- PDF URL
-
https://thesai.org/Downloads/Volume14No8/Paper_37-Prediction_of_Cryptocurrency_Price_using_Time_Series_Data.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://thesai.org/Downloads/Volume14No8/Paper_37-Prediction_of_Cryptocurrency_Price_using_Time_Series_Data.pdfDirect OA link when available
- Concepts
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Cryptocurrency, Mean squared error, Computer science, Artificial intelligence, Recurrent neural network, Machine learning, Mean absolute error, Time series, Deep learning, Volatility (finance), Series (stratigraphy), Artificial neural network, Convolutional neural network, Algorithm, Econometrics, Statistics, Mathematics, Biology, Computer security, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 5, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
46Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | International Journal of Advanced Computer Science and Applications |
| primary_location.landing_page_url | https://doi.org/10.14569/ijacsa.2023.0140837 |
| publication_date | 2023-01-01 |
| publication_year | 2023 |
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