Optional freqA frequency indicator for the given timestamp_column. See https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#period-aliases for a description of the allowed values. If not provided, we will attempt to infer it from the data.
Optional id_Columns that define a unique key for timeseries. This is similar to a compound primary key in a database table.
Optional target_An array of column headings which constitute the target variables in the data. These are the data that will be forecasted.
A valid column in the data that should be treated as the timestamp. Although not absolutely necessary, if using calendar dates (simple integer time offsets are also allowed), users should consider using a format such as ISO 8601 that includes a UTC offset (e.g., '2024-10-18T01:09:21.454746+00:00'). This will avoid potential issues such as duplicate dates appearing due to daylight savings change overs. There are many date formats in existence and inferring the correct one can be a challenge so please do consider adhering to ISO 8601.
Contains metadata about your timeseries data input.