Car-Parts

Monthly sales car parts. 2674 series. Jan 1998 - Mar 2002. Extracted from 'expsmooth' R package. There are 2677 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_X (X from 0 to 2673): The values of the time series, which will be used for the forecasting task. Preprocessing: 1 - Transposed the dataset. 2 - Renamed columns (originally id of car parts) to 'value_X' with X from 0 to 2673. 3 - Standardize the date to the format %Y-%m-%d. 4 - Created column 'id_series', with value 0, there is only one multivariate series. 5 - Ensured that there are no missing dates and that they are evenly spaced (monthly). 6 - Created column 'time_step' with increasing values of the time_step for the time series. 7 - Filled NaN values with 0. For some values, after some months, there are only NaNs, which could indicate that the car part was not sold anymore, thus 0 makes sense. 8 - Casted 'value_X' columns to int, and 'id_series' as 'category'.