load_from_tsf_file

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

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_name: str

The full path to the .tsf file.

replace_missing_vals_with: str, default=”NAN”

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

value_column_name: str, default=”series_value”

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

Returns:

loaded_data: pd.DataFrame

The converted dataframe containing the time series.

metadata: dict

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