{ "data_id": "43601", "name": "Daily-Wheat-Price", "exact_name": "Daily-Wheat-Price", "version": 1, "version_label": "v1.0", "description": "Context\nLast time I built an LSTM price prediction for Corn, but the result is not satisfactory. I would like to try other algorithm and data. So I decided to use Wheat price for the exercise. And this time the data is more in length of time (9 years).\nContent\nThe daily wheat dataset is from 2009-10-14 to 2018-03-12. It is downloaded from investing.com or Quantapi also have a API for it. \nAcknowledgements\nhttps:\/\/www.investing.com\/commodities\/us-wheat\n\nhttps:\/\/quantapi.co\/\nInspiration\nTo the extent that you can find ways where you're making predictions, there's no substitute for testing yourself on real-world situations that you don't know the answer to in advance. \nNate Silver", "format": "arff", "uploader": "Dustin Carrion", "uploader_id": 30123, "visibility": "public", "creator": null, "contributor": null, "date": "2022-03-24 00:38:02", "update_comment": null, "last_update": "2022-03-24 00:38:02", "licence": "CC0: Public Domain", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/22102426\/dataset", "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "Daily-Wheat-Price", "Context Last time I built an LSTM price prediction for Corn, but the result is not satisfactory. I would like to try other algorithm and data. So I decided to use Wheat price for the exercise. And this time the data is more in length of time (9 years). Content The daily wheat dataset is from 2009-10-14 to 2018-03-12. It is downloaded from investing.com or Quantapi also have a API for it. Acknowledgements https:\/\/www.investing.com\/commodities\/us-wheat https:\/\/quantapi.co\/ Inspiration To the exten " ], "weight": 5 }, "qualities": { "NumberOfInstances": 2272, "NumberOfFeatures": 5, "NumberOfClasses": null, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 4, "NumberOfSymbolicFeatures": 0, "Dimensionality": 0.0022007042253521128, "PercentageOfNumericFeatures": 80, "MajorityClassPercentage": null, "PercentageOfSymbolicFeatures": 0, "MajorityClassSize": null, "MinorityClassPercentage": null, "MinorityClassSize": null, "NumberOfBinaryFeatures": 0, "PercentageOfBinaryFeatures": 0, "PercentageOfInstancesWithMissingValues": 0, "AutoCorrelation": null, "PercentageOfMissingValues": 0 }, "tags": [ { "uploader": "38960", "tag": "Computer Systems" }, { "uploader": "38960", "tag": "Machine Learning" } ], "features": [ { "name": "date", "index": "0", "type": "string", "distinct": "2272", "missing": "0" }, { "name": "open", "index": "1", "type": "numeric", "distinct": "1611", "missing": "0", "min": "392", "max": "942", "mean": "593", "stdev": "135" }, { "name": "high", "index": "2", "type": "numeric", "distinct": "1565", "missing": "0", "min": "396", "max": "945", "mean": "601", "stdev": "137" }, { "name": "low", "index": "3", "type": "numeric", "distinct": "1523", "missing": "0", "min": "381", "max": "926", "mean": "586", "stdev": "133" }, { "name": "close", "index": "4", "type": "numeric", "distinct": "1569", "missing": "0", "min": "384", "max": "944", "mean": "593", "stdev": "135" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 0, "nr_of_downloads": 0, "total_downloads": 0, "reach": 0, "reuse": 0, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 0 }