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covariate and forecasting target #9
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For our current benchmarking model implementations in this repo, we have considered all series as target series, not covariates. However, you can use the name of the series (series_name attribute) to distinguish between target series and covariate series. Within benchmarking functions, then you can input the series into models as targets and covariates separetly. Although our .tsf format supports the inclusion of covariate series, the models in our repo do not have this implementation as our focus was to run the benchmarks in their most simple format without covariates. |
right, so my question was how does the |
Sure, you can use a prefix with a series_name to identify whether it is a covariate series. We have done something similar in one of our latest project available at https://github.com/rakshitha123/SETAR_Trees. If you see a dataset with covariates in the "datasets" folder (e.g. Rossmann with covariates), you'll find covariate series in their starting with prefix such as "Customers", "Open" and "Promo" where the target series start with the prefix "T". |
thanks @rakshitha123 I'll have a look, I don't have my own data but wish to use your repository for my models... the dataset convention I have is a bit more verbose in defining the covariates as well as categorical features explicitly and I am trying to convert from the tsf format to my convention... |
Hello! I had a question regarding the forecasting target vs. additional covariates in the dataset. For example, in the
temperature_rain
dataset there are some fields that I assume are the forecast target from the names of the column while others are covariates... How does thetst
format distinguish between those?The text was updated successfully, but these errors were encountered: