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, sampler_weights: None | str = None) → 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 be 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 (sampler_weights) – if not None the

  • realization (time serie dataset is considered composed by omogeneus timeseries coming from different)

  • 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)

  • group – if it is a column name it will be used as weight for the sampler. Careful that the weight of the sample is the weight value of the fist target value (index)