Supporting data for the manuscript "TempoStackNet: An Advanced Stacking-Based Network for Enhanced Multi-Step Cryptocurrencies Prediction Using Temporal Attention Mechanisms". Mechanisms
The data was downloaded from Yahoo Finance and divided into three main datasets.The first one is the USD price of Bitcoin (btc_data.csv), ranging from February 24, 2022, 00:00 to February 21, 2024, 23:00, comprising 17,165 data points with a time interval of 1 hour.The second dataset is the USD pric...
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| description | The data was downloaded from Yahoo Finance and divided into three main datasets.The first one is the USD price of Bitcoin (btc_data.csv), ranging from February 24, 2022, 00:00 to February 21, 2024, 23:00, comprising 17,165 data points with a time interval of 1 hour.The second dataset is the USD price of Ethereum (en_data.csv), spanning from February 24, 2022, 00:00 to February 21, 2024, 23:00, consisting of 12,342 data points with a time interval of 1 hour.The third dataset is the S&P 500 index (sp500_data.csv), covering the period from February 24, 2022, 00:00 to February 21, 2024, 23:00, containing 11,221 data points with a time interval of 1 hour.Regarding data partitioning, 80% of the data is allocated for training, while the remaining 20% is for testing. We employ a multi-step forecasting approach with a time step set to 32 steps.
The data was downloaded from Yahoo Finance and divided into three main datasets.The first one is the USD price of Bitcoin (btc_data.csv), ranging from February 24, 2022, 00:00 to February 21, 2024, 23:00, comprising 17,165 data points with a time interval of 1 hour.The second dataset is the USD price of Ethereum (en_data.csv), spanning from February 24, 2022, 00:00 to February 21, 2024, 23:00, consisting of 12,342 data points with a time interval of 1 hour.The third dataset is the S&P 500 index (sp500_data.csv), covering the period from February 24, 2022, 00:00 to February 21, 2024, 23:00, containing 11,221 data points with a time interval of 1 hour.Regarding data partitioning, 80% of the data is allocated for training, while the remaining 20% is for testing. We employ a multi-step forecasting approach with a time step set to 32 steps. |
| doi_str_mv | 10.57760/sciencedb.09037 |
| format | Dataset |
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The data was downloaded from Yahoo Finance and divided into three main datasets.The first one is the USD price of Bitcoin (btc_data.csv), ranging from February 24, 2022, 00:00 to February 21, 2024, 23:00, comprising 17,165 data points with a time interval of 1 hour.The second dataset is the USD price of Ethereum (en_data.csv), spanning from February 24, 2022, 00:00 to February 21, 2024, 23:00, consisting of 12,342 data points with a time interval of 1 hour.The third dataset is the S&P 500 index (sp500_data.csv), covering the period from February 24, 2022, 00:00 to February 21, 2024, 23:00, containing 11,221 data points with a time interval of 1 hour.Regarding data partitioning, 80% of the data is allocated for training, while the remaining 20% is for testing. We employ a multi-step forecasting approach with a time step set to 32 steps.</description><identifier>DOI: 10.57760/sciencedb.09037</identifier><language>eng</language><publisher>Science Data Bank</publisher><subject>Computer science and technology ; cryptocurrencies prediction ; Economics ; Math ; temporal attention mechanisms ; time series data</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,1894</link.rule.ids><linktorsrc>$$Uhttps://commons.datacite.org/doi.org/10.57760/sciencedb.09037$$EView_record_in_DataCite.org$$FView_record_in_$$GDataCite.org$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>HAN-YUE WANG</creatorcontrib><title>Supporting data for the manuscript "TempoStackNet: An Advanced Stacking-Based Network for Enhanced Multi-Step Cryptocurrencies Prediction Using Temporal Attention Mechanisms". Mechanisms</title><description>The data was downloaded from Yahoo Finance and divided into three main datasets.The first one is the USD price of Bitcoin (btc_data.csv), ranging from February 24, 2022, 00:00 to February 21, 2024, 23:00, comprising 17,165 data points with a time interval of 1 hour.The second dataset is the USD price of Ethereum (en_data.csv), spanning from February 24, 2022, 00:00 to February 21, 2024, 23:00, consisting of 12,342 data points with a time interval of 1 hour.The third dataset is the S&P 500 index (sp500_data.csv), covering the period from February 24, 2022, 00:00 to February 21, 2024, 23:00, containing 11,221 data points with a time interval of 1 hour.Regarding data partitioning, 80% of the data is allocated for training, while the remaining 20% is for testing. We employ a multi-step forecasting approach with a time step set to 32 steps.
The data was downloaded from Yahoo Finance and divided into three main datasets.The first one is the USD price of Bitcoin (btc_data.csv), ranging from February 24, 2022, 00:00 to February 21, 2024, 23:00, comprising 17,165 data points with a time interval of 1 hour.The second dataset is the USD price of Ethereum (en_data.csv), spanning from February 24, 2022, 00:00 to February 21, 2024, 23:00, consisting of 12,342 data points with a time interval of 1 hour.The third dataset is the S&P 500 index (sp500_data.csv), covering the period from February 24, 2022, 00:00 to February 21, 2024, 23:00, containing 11,221 data points with a time interval of 1 hour.Regarding data partitioning, 80% of the data is allocated for training, while the remaining 20% is for testing. We employ a multi-step forecasting approach with a time step set to 32 steps.</description><subject>Computer science and technology</subject><subject>cryptocurrencies prediction</subject><subject>Economics</subject><subject>Math</subject><subject>temporal attention mechanisms</subject><subject>time series data</subject><fulltext>true</fulltext><rsrctype>dataset</rsrctype><creationdate>2024</creationdate><recordtype>dataset</recordtype><sourceid>PQ8</sourceid><recordid>eNqdT7FOwzAQzcKACjvjqXuCqwqisoWqiKUIKWW2jHOlpya2db6A-mt8HY5BiJnp9O7de_deUVwtVHVT17fqOlpCZ7F7rdRKLevz4rMdQ_As5N6gM2Jg7xnkgDAYN0bLFATmOxyCb8XY4xPKHTQOmu7dTD6Qt0lc3puYYOI_PB-zy8Ydvm-2Yy9UtoIB1nwK4u3InGIQRnhm7MgKeQcvcQqRf7HpoRFBl4kt2uREcYjz6g-4KM72po94-TNnhXrY7NaP5dTDkqAOTIPhk14onevr3_o611_-Q_IFR71w0w</recordid><startdate>20240526</startdate><enddate>20240526</enddate><creator>HAN-YUE WANG</creator><general>Science Data Bank</general><scope>DYCCY</scope><scope>PQ8</scope></search><sort><creationdate>20240526</creationdate><title>Supporting data for the manuscript "TempoStackNet: An Advanced Stacking-Based Network for Enhanced Multi-Step Cryptocurrencies Prediction Using Temporal Attention Mechanisms". Mechanisms</title><author>HAN-YUE WANG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-datacite_primary_10_57760_sciencedb_090373</frbrgroupid><rsrctype>datasets</rsrctype><prefilter>datasets</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer science and technology</topic><topic>cryptocurrencies prediction</topic><topic>Economics</topic><topic>Math</topic><topic>temporal attention mechanisms</topic><topic>time series data</topic><toplevel>online_resources</toplevel><creatorcontrib>HAN-YUE WANG</creatorcontrib><collection>DataCite (Open Access)</collection><collection>DataCite</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>HAN-YUE WANG</au><format>book</format><genre>unknown</genre><ristype>DATA</ristype><title>Supporting data for the manuscript "TempoStackNet: An Advanced Stacking-Based Network for Enhanced Multi-Step Cryptocurrencies Prediction Using Temporal Attention Mechanisms". Mechanisms</title><date>2024-05-26</date><risdate>2024</risdate><abstract>The data was downloaded from Yahoo Finance and divided into three main datasets.The first one is the USD price of Bitcoin (btc_data.csv), ranging from February 24, 2022, 00:00 to February 21, 2024, 23:00, comprising 17,165 data points with a time interval of 1 hour.The second dataset is the USD price of Ethereum (en_data.csv), spanning from February 24, 2022, 00:00 to February 21, 2024, 23:00, consisting of 12,342 data points with a time interval of 1 hour.The third dataset is the S&P 500 index (sp500_data.csv), covering the period from February 24, 2022, 00:00 to February 21, 2024, 23:00, containing 11,221 data points with a time interval of 1 hour.Regarding data partitioning, 80% of the data is allocated for training, while the remaining 20% is for testing. We employ a multi-step forecasting approach with a time step set to 32 steps.
The data was downloaded from Yahoo Finance and divided into three main datasets.The first one is the USD price of Bitcoin (btc_data.csv), ranging from February 24, 2022, 00:00 to February 21, 2024, 23:00, comprising 17,165 data points with a time interval of 1 hour.The second dataset is the USD price of Ethereum (en_data.csv), spanning from February 24, 2022, 00:00 to February 21, 2024, 23:00, consisting of 12,342 data points with a time interval of 1 hour.The third dataset is the S&P 500 index (sp500_data.csv), covering the period from February 24, 2022, 00:00 to February 21, 2024, 23:00, containing 11,221 data points with a time interval of 1 hour.Regarding data partitioning, 80% of the data is allocated for training, while the remaining 20% is for testing. We employ a multi-step forecasting approach with a time step set to 32 steps.</abstract><pub>Science Data Bank</pub><doi>10.57760/sciencedb.09037</doi><oa>free_for_read</oa></addata></record> |
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| identifier | DOI: 10.57760/sciencedb.09037 |
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| language | eng |
| recordid | cdi_datacite_primary_10_57760_sciencedb_09037 |
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| subjects | Computer science and technology cryptocurrencies prediction Economics Math temporal attention mechanisms time series data |
| title | Supporting data for the manuscript "TempoStackNet: An Advanced Stacking-Based Network for Enhanced Multi-Step Cryptocurrencies Prediction Using Temporal Attention Mechanisms". Mechanisms |
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