Closed ZhangAllen98 closed 2 months ago
Hi @ZhangAllen98, yes you should do it as you described here:
Should I transform the multivariate time series (1 series with 370 components)to multiple time series (370 series with each having 1 component), and for each series to prepare a covariates time series with 3 components. It seems this way is straightforward.
For this, simply create a list of single column time series from the electricity dataset:
from darts.datasets import ElectricityDataset
series = ElectricityDataset().load()
series = [series[col] for col in series.columns]
And then create a covariates TimeSeries
for each of the series in series
and pass them as a list of covariates TimeSeries
of the same length (370) to the models.
In the multi-time-series example, Note that
ElectricityDataset
contains measurements of electric power comsumption (in kW) for 370 clients with a sampling rate of 15 minutes. For this case, if I want to add each clients with future covariates including temperature, wind speed, and humidity etc, How should I do.Should I transform the multivariate time series (1 series with 370 components)to multiple time series (370 series with each having 1 component), and for each series to prepare a covariates time series with 3 components. It seems this way is straightforward.
Or just keep the multivariate time series unchanged, and prepare the covariates time series, If so, how should I prepare this kind of covariates time series to let the model know which three covariates belong to which client?
Is there any demo code for this kind of case. Thanks