From 709c77c87399b3d4d558226d2b47a2aba26004cb Mon Sep 17 00:00:00 2001
From: Ryuta Yoshimatsu <107132414+ryuta-yoshimatsu@users.noreply.github.com>
Date: Fri, 14 Jun 2024 15:22:17 +0200
Subject: [PATCH] Update README.md

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 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

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@@ -186,7 +186,7 @@ We encourage you to read through [examples/global_daily.py](https://github.com/d
 
 ### Foundation Models
 
-Foundation time series models are transformer based models pretrained on millions or billions of time points. These models can produce analysis (i.e. forecasting, anomaly detection, classification) on an unforeseen time series without training or tuning. We support open source models from multiple sources: [chronos](https://github.com/amazon-science/chronos-forecasting), [moirai](https://blog.salesforceairesearch.com/moirai/), and [moment](https://github.com/moment-timeseries-foundation-model/moment). Covariates (i.e. exogenous regressors) and fine-tuning are currently not yet supported. This is a rapidly changing field, and we are working on updating the supported models and new features as the field evolves.
+Foundation time series models are transformer based models pretrained on millions or billions of time points. These models can perform analysis (i.e. forecasting, anomaly detection, classification) on a previously unseen time series without training or tuning. We support open source models from multiple sources: [chronos](https://github.com/amazon-science/chronos-forecasting), [moirai](https://blog.salesforceairesearch.com/moirai/), and [moment](https://github.com/moment-timeseries-foundation-model/moment). Covariates (i.e. exogenous regressors) and fine-tuning are currently not yet supported. This is a rapidly changing field, and we are working on updating the supported models and new features as the field evolves.
 
 To get started, attach the [examples/foundation_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/foundation_daily.py) notebook to a cluster running [DBR 14.3 LTS for ML](https://docs.databricks.com/en/release-notes/runtime/index.html) or later versions. We recommend using a single-node cluster with multiple GPU instances such as [g4dn.12xlarge [T4]](https://aws.amazon.com/ec2/instance-types/g4/) on AWS or [Standard_NC64as_T4_v3](https://learn.microsoft.com/en-us/azure/virtual-machines/nct4-v3-series) on Azure. Multi-node setup is currently not supported.