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The datasets are divided into two main categories: datasets that include the auction period and datasets that do not. For each of these two categories we provide three normalization set-ups based on z-score, min-max, and decimal-precision normalization. Since we followed the anchored cross-validation method for 10 days for 5 stocks, the user can find nine (cross-fold) datasets for each normalization set-up for training and testing. Every training and testing dataset contains information for all the stocks. For example, the first fold contains one-day of training and one-day of testing for all the five stocks. The second fold contains the training dataset for two days and the testing dataset for one day. The two-days information the training dataset has is the training and testing from the first fold and so on.\r\n\r\nThe title of the .txt files contains the information in the following order:\r\n\r\n1.  training or testing set\r\n2.  with or without auction period\r\n3.  type of the normalization setup\r\n4.  fold number (from 1 to 9) based on the above cross-validation method\r\n\r\nATTENTION: The given files contain both the feature set and the labels. From row 1 to row 144 we provide the features (see 'Benchmark Dataset for Mid-Price Prediction of Limit Order Book Data' for the description) and from row 145 to row 149 we provide labels for 5 classification problems.  Labels (row 145 to the end) have the following explanation ‘1’ is for up-movement, ‘2’ is for stationary condition and ‘3’ is for down-movement.\r\n\r\n\r\n\r\n## Motivation\r\n\r\nThese are the first publicly available datasets that contain representations and annotations for a limit order book (LOB) in the High Frequency Trading universe.\r\n\r\n## Tests\r\n\r\nWe provide baselines for these datasets based on linear and non-linear regression methods.\r\n\r\n## Acknowledgment\r\n\r\nThe research leading to these results has received funding from the H2020 Project BigDataFinance MSCA-ITN-ETN 675044 (http://bigdatafinance.eu), Training for Big Data in Financial Research and Risk Management."},"field_of_science":[],"fileset":{"storage_service":"ida","csc_project":"tty8021","total_files_count":1,"total_files_size":1864361899},"infrastructure":[],"issued":"2017-06-01","keyword":[],"language":[{"id":"ec748146-3403-4a7f-adfe-bdbb1b889372","url":"http://lexvo.org/id/iso639-3/eng","in_scheme":"http://lexvo.org/id/","pref_label":{"en":"English","fi":"englanti","sv":"engelska"}}],"metadata_owner":{"id":"8dc3fd0f-49c6-4e9f-9a22-8795cb5c4ac5","organization":"tuni.fi","admin_organization":"tuni.fi"},"other_identifiers":[],"persistent_identifier":"urn:nbn:fi:csc-kata20170601153214969115","pid_generated_by_fairdata":true,"projects":[{"id":"e1b6ee2d-c8ae-4dda-8fab-fe0ff59aca92","title":{"und":"BigDataFinance"},"participating_organizations":[],"funding":[]}],"provenance":[],"relation":[],"remote_resources":[],"spatial":[{"geographic_name":"Finland","reference":{"id":"684b98e8-d5c1-4a59-9247-4793cbbd38e4","url":"http://www.yso.fi/onto/yso/p94426","in_scheme":"http://www.yso.fi/onto/yso/places","pref_label":{"en":"Finland","fi":"Suomi","sv":"Finland"},"as_wkt":"POINT (27 65)"},"custom_wkt":["POINT (27 65)"],"geolocations":{"type":"FeatureCollection","features":[{"type":"Feature","geometry":{"type":"Point","coordinates":[27.0,65.0]},"bbox":[27.0,65.0,27.0,65.0]}]}}],"state":"published","temporal":[],"theme":[{"id":"9c84774d-8d33-4b4f-8cdc-2d0b178452f6","url":"http://www.yso.fi/onto/koko/p32651","in_scheme":"http://www.yso.fi/onto/koko/","pref_label":{"en":"finance","fi":"rahoitus","sv":"finansiering"}}],"title":{"en":"Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods"},"created":"2019-06-27T07:08:43Z","modified":"2017-06-01T12:20:11Z","dataset_versions":[{"id":"73eb48d7-4dbc-4a10-a52a-da745b47a649","title":{"en":"Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods"},"persistent_identifier":"urn:nbn:fi:csc-kata20170601153214969115","state":"published","created":"2019-06-27T07:08:43Z","version":1}],"published_revision":1,"version":1,"api_version":1,"metadata_repository":"Fairdata","record_created":"2025-02-10T16:51:00Z","record_modified":"2025-02-10T16:51:01Z"}