Monthly patient count for products that are related to medical problems.
From original source:
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Monthly patient count for products that are related to medical problems. There are 767 time series
that had a mean count of at least 10 and no zeros.
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Extracted from 'expsmooth' R package (.csv available on official website)
There are 6 columns:
id_series: The id of the time series.
date: The date of the time series in the format "%Y-%m-%d".
time_step: The time step on the time series.
value_0: The values of the time series, which will be used for the forecasting task.
covariate_X (X from 0 to 1): Covariate values of the time series, tied to the 'id_series'. Not interested in forecasting, but can help with the forecasting task.
Preprocessing:
1 - Melted the dataset with indentifiers 'MPriceHospLOS2000_SKUCode', 'MPriceHospLOS2000_RootEntityCode', obtaining columns 'date' and 'value'.
2 - Standardize the date to the format %Y-%m-%d.
3 - Renamed columns 'MPriceHospLOS2000_SKUCode', 'MPriceHospLOS2000_RootEntityCode' to 'covariate_0' and 'covariate_1'.
4 - Created column 'id_series' from covariate_0' and 'covariate_1' with index from 0 to 766.
5 - Created column 'time_step' with increasing values of the time_step for the time series.
6 - Casted 'value' columns to int, and defined 'id_series', covariate_0' and 'covariate_1' as 'category'.