dsipts.TimeSeries.load_signal

TimeSeries.load_signal(data: DataFrame, enrich_cat: List[str] = [], past_variables: List[str] = [], future_variables: List[str] = [], target_variables: List[str] = [], cat_past_var: List[str] = [], cat_fut_var: List[str] = [], check_past: bool = True, group: None | str = None, check_holes_and_duplicates: bool = True, silly_model: bool = False) → None
This is a crucial point in the data structure. We expect here to have a dataset with time as timestamp.
There are some checks:

1- the duplicates will tbe removed taking the first instance

2- the frequency will the inferred taking the minumum time distance between samples

3- the dataset will be filled completing the missing timestamps

Parameters:

  • data (pd.DataFrame) – input dataset the column indicating the time must be called time

  • enrich_cat (List[str], optional) – it is possible to let this function enrich the dataset for example adding the standard columns: hour, dow, month and minute. Defaults to [].

  • past_variables (List[str], optional) – list of column names of past variables not available for future times . Defaults to [].

  • future_variables (List[str], optional) – list of future variables available for tuture times. Defaults to [].

  • target_variables (List[str], optional) – list of the target variables. They will added to past_variables by default unless check_past is false. Defaults to [].

  • cat_past_var (List[str], optional) – list of the past categorical variables. Defaults to [].

  • cat_future_var (List[str], optional) – list of the future categorical variables. Defaults to [].

  • check_past (bool, optional) – see target_variables. Defaults to True.

  • group (str or None, optional) – if not None the time serie dataset is considered composed by omogeneus timeseries coming from different realization (for example point of sales, cities, locations) and the relative series are not splitted during the sample generation. Defaults to None

  • check_holes_and_duplicates (bool, optional) – if False duplicates or holes will not checked, the dataloader can not correctly work, disable at your own risk. Defaults True

  • silly_model (bool, optional) – if True, target variables will be added to the pool of the future variables. This can be useful to see if information passes throught the decoder part of your model (if any)