From 6da174d703aab0ec3c6ae31e821c3d92bea800d8 Mon Sep 17 00:00:00 2001 From: Killer3048 Date: Thu, 10 Apr 2025 10:47:44 +0700 Subject: [PATCH] add information about install pax/paxlib for covariate support in section `Covariates Support` --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/README.md b/README.md index 31208e5..fbe0d9f 100644 --- a/README.md +++ b/README.md @@ -246,6 +246,12 @@ forecast_df = tfm.forecast_on_df( We now have an external regressors library on top of TimesFM that can support static covariates as well as dynamic covariates available in the future. We have an usage example in [notebooks/covariates.ipynb](https://github.com/google-research/timesfm/blob/master/notebooks/covariates.ipynb). +If you plan to use the **`forecast_with_covariates`** on timesfm `torch` version, you need to install **JAX** and **jaxlib**. +You must manually install the dependencies for **`forecast_with_covariates`** support: +``` +pip install jax jaxlib +``` + Let's take a toy example of forecasting sales for a grocery store: **Task:** Given the observed the daily sales of this week (7 days), forecast the daily sales of next week (7 days).