load_from_tsf_file

load_from_tsf_file(full_file_path_and_name, replace_missing_vals_with='NaN', value_column_name='series_value', return_type='tsf_default')

Convert the contents in a .tsf file into a dataframe.

This code was extracted from https://github.com/rakshitha123/TSForecasting/blob/master/utils/data_loader.py.

Parameters:

full_file_path_and_namestr

The full path to the .tsf file.

replace_missing_vals_withstr, default=”NAN”

A term to indicate the missing values in series in the returning dataframe.

value_column_namestr, default=”series_value”

Any name that is preferred to have as the name of the column containing series values in the returning dataframe.

return_typestr - “pd_multiindex_hier” or “tsf_default” (default)

• “tsf_default” = container that faithfully mirrors tsf format from the original

  implementation in: https://github.com/rakshitha123/TSForecasting/ blob/master/utils/data_loader.py.

Returns:

loaded_datapd.DataFrame

The converted dataframe containing the time series.

metadatadict

The metadata for the forecasting problem. The dictionary keys are: “frequency”, “forecast_horizon”, “contain_missing_values”, “contain_equal_length”

Raises:

URLError or HTTPError

If the website is not accessible.

ValueError

If a dataset name that does not exist on the repo is given or if a webpage is requested that does not exist.