Granite TimeSeries PatchTST FM Model Card Model Description PatchTST was originally released prior to the interest in creating pre trained, zero shot time series foundation models that were capable of state of the art performance on out of sample datasets. PatchTST FM (patched time series transformer based foundation model) essentially has the architectural simplicity of PatchTST, but differs in some crucial ways. Coupled with a revised training strategy and a significantly larger training corpus, we are able to train a model that achieves state of the art results on GiftEval (see below for recommended filters to view this on the leaderboard). The architecture incorporates the following changes: residual blocks in the input and output projections a quantile head to support probabilistic forecasting enhanced training strategies incorporating contiguous patch masking, and random masking in the forecast period trained with reconstruction loss objective forecast at inference time is cast as "reconstruction" of the masked forecast period while past context is not masked. If the context period has missing values, both the missing timepoints and forecast timepoints are filled in thus prov…
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