Open chrisconst2 opened 7 months ago
@kashif
@chrisconst2 so due to the nature of patching the only potential covariates that can be included with PatchTST and I believe PatchTSMixer are going to be static features and static real-valued covariates... I believe the method is not too flexible in being able to accommodate these extra bells and whistle... what kind of exogenous variables were you thinking of using?
@kashif There are many exogenous variables can improve the perfomace in stock / sales forecasting. For stock forecasting, the typical exogenous variables are fundamental indicator : year-on-year (YoY) growth, profit margin, earnings per share (EPS), price-to-earnings (PE) ratio, and profit after tax (PAT), etc ... For sales forecasting, could be : discount rate, with gift, gift value , balabala ...
@eromoe of course, however, this method doesn't have the inductive bias to incorporate covariates (especially temporal ones) in a straight-forward fashion. I have a variant of patch-tst called lag-tst that doesn't have this issue... https://github.com/awslabs/gluonts/tree/dev/src/gluonts/torch/model/lag_tst
Feature request
Include categorical features and exogenous variables as input for the PatchTST and PatchTSMixer timeseries foundation models
Motivation
Categorical features and exogenous variables are key components in timeseries modelling
Your contribution
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