Data
Daily-Wheat-Price

Daily-Wheat-Price

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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  • Computer Systems Machine Learning
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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 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. Nate Silver

5 features

datestring2272 unique values
0 missing
opennumeric1611 unique values
0 missing
highnumeric1565 unique values
0 missing
lownumeric1523 unique values
0 missing
closenumeric1569 unique values
0 missing

19 properties

2272
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
4
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
80
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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