Your forecast plugin looks great, but the flow takes all values as a single timeseries. Is it possible to specify a column to partition the data on? It would be nice to train and predict forecast models for multiple entities in one go.
Thanks for your interest in this recent plugin release.
If you want run the recipes to get multiple forecasting models per category (e.g. per product or store), you will need partitioning. That requires to have all datasets partitioned by 1 dimension for the category, using the discrete dimension feature in Dataiku. If the input data is not partitioned, you can use a Sync recipe to repartition it, as explained in this article.
Hope it helps,
We are proud to announce that we just released a new Forecast plugin. Among other features, it supports multivariate forecasting natively, with no need to partition your data 😎
Forecasting sales across 1000s of stores and departments is now as simple as this:
On top of this, you will benefit from the latest Deep Learning models from GluonTS such as DeepAR and Transformer.
Give it a try, let us know what you think, and reshare if you like it ❤️
Hi @Alex_Combessie !
The new forecast plugin is just amazing, It makes multivariate Time series forecasting much easier.
I'm wondering why classical Algo(Included in the old version of the plugin (legacy)) like Exponential smoothing, is not part of the new one?
for instance, if I want to use Exp smoothing, I should partition my multivariate Data set and use the legacy forecast plugin while it would be good if I could directly use the new one.
Thanks for the kind words.
When developing the new Forecast plugin, we have performed benchmarks on performance and runtime to help choose which algorithms to include by default. We are very much open to include new ones in future updates.
In the specific case of Exponential Smoothing (ETS) it is available as a modeling option in the "Seasonal Trend" model, as explained here: https://www.dataiku.com/product/plugins/timeseries-forecast/#stat-models. It removes seasonality, and then applies an ETS model from statsmodels.
Hope it helps,