specify forecast horizon

README.md

@@ -9,7 +9,7 @@ metrics:
 
 The [`PatchTSMixer`](https://huggingface.co/docs/transformers/model_doc/patchtsmixer) is a lightweight and fast multivariate time series forecasting model with state-of-the-art performance on benchmark datasets. 
 In this context, we offer a pre-trained `PatchTSMixer` model encompassing all seven channels of the `ETTh1` dataset. 
-This specific pre-trained model yields a Mean Squared Error (MSE) of 0.37 on the test split of the `ETTh1` dataset.
+This particular pre-trained model produces a Mean Squared Error (MSE) of 0.37 on the `test` split of the `ETTh1` dataset when forecasting 96 hours into the future with a historical data window of 512 hours.
 
 For training and evaluating a `PatchTSMixer` model, you can refer to [this notebook](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/patch_tsmixer_getting_started.ipynb).
 
@@ -38,7 +38,7 @@ TSMixer is a lightweight neural architecture exclusively composed of multi-layer
 ## Uses
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-This pre-trained model can be utilized for fine-tuning or evaluation with any Electrical Transformer dataset that shares the same channels as the `ETTh1` dataset, namely: `HUFL, HULL, MUFL, MULL, LUFL, LULL, OT`. It is important to ensure that the data is normalized. For detailed information on data pre-processing, please refer to the paper or the demo.
+This pre-trained model can be employed for fine-tuning or evaluation using any Electrical Transformer dataset that has the same channels as the `ETTh1` dataset, specifically: `HUFL, HULL, MUFL, MULL, LUFL, LULL, OT`. The model is designed to predict the next 96 hours based on the input values from the preceding 512 hours. It is crucial to normalize the data. For a more comprehensive understanding of data pre-processing, please consult the paper or the demo.
 
 ## How to Get Started with the Model