My favorite way to prototype a dashboard is to use Streamlit to lay things out and serve it and then use Altair [https://altair-viz.github.io/] to generate the Vega-Lite plots in Python. Then if you need to move to something besides Python to productionize, you can produce the same Vega-Lite definitions using the framework of your choice.
They do mention covariates in section 6.1 - specifically how this method doesn’t support them but ideas on how they could in the future such as via stacking:
> In this work, we have focused on univariate time series forecasting since it constitutes the most common of real-world time series use-cases. Nevertheless, practical forecasting tasks often involve additional information that must be taken into account. One example involves covariates, that can be either time-independent (e.g., color of the product) or time-varying (e.g., on which days the product is on sale). Another closely related problem is multivariate forecasting, where historic values of one time series (e.g., interest rates) can influence the forecast for another time series (e.g., housing prices). The number of covariates or multivariate dimensions can vary greatly across tasks, which makes it challenging to train a single model that can handle all possible combinations. A possible solution may involve training task-specific adaptors that inject the covariates into the pretrained forecasting model (Rahman et al., 2020). As another option, we can build stacking ensembles (Ting & Witten, 1997) of Chronos and other light-weight models that excel at handling covariates such as LightGBM (Ke et al., 2017).
Thanks for this! I've been going crazy for months that Twitter threads and replies are totally broken if you are logged out (which I always am since deleting my account in December). And now Nitter has led me to LibRedirect[1] which not only automatically redirects Twitter links but also lots of other common links like TikTok.
Lots of great suggestions here, but one I haven't seen is providing deep links. Let users share the exact state of their dashboard with others, ideally without requiring some convoluted system of logging in and sharing things. We implemented it by allowing a json config in the url, then providing a button to copy a shortened URL containing the whole config.
Original creator of (the now woefully dated-looking) GBD Compare [https://vizhub.healthdata.org/gbd-compare/] here, where we found this super useful since we had so many controls that it could take a lot of clicking (and knowledge of the UI) to recreate a graph someone else was looking at. It really helped with reach, as folks could email/tweet their specific view then others could use that as a starting point to dive in without starting from scratch or having to create an account.