push poetry version

.github/workflows/main.yml

@@ -23,7 +23,7 @@ jobs:
       # e.g. poetry version 0.1.${{ github.run_number }}
       - name: Set Version number
         run: |
-          poetry version 1.2.5
+          poetry version 1.2.6
       - name: Build and Publish to PyPI
         run: |
           poetry config pypi-token.pypi ${{ secrets.PYPI_API_TOKEN }}

README.md

@@ -23,19 +23,17 @@ We recommend at least 32GB RAM to load TimesFM dependencies.
 
 ## Checkpoint timesfm-1.0-200m (-pytorch)
 
-timesfm-1.0-200m is the first open model checkpoint:
+timesfm-1.0-200m is our first open model checkpoint:
 
 - It performs univariate time series forecasting for context lengths up to 512 timepoints and any horizon lengths, with an optional frequency indicator.
 - It focuses on point forecasts, and does not support probabilistic forecasts. We experimentally offer quantile heads but they have not been calibrated after pretraining.
-- It requires the context to be contiguous (i.e. no "holes"), and the context and the horizon to be of the same frequency.
 
 ## Checkpoint timesfm-2.0-500m (-jax/-pytorch)
 
-timesfm-2.0-500m is the second open model checkpoint:
+timesfm-2.0-500m is our second open model checkpoint:
 
 - It performs univariate time series forecasting for context lengths up to 2048 timepoints and any horizon lengths, with an optional frequency indicator.
 - It focuses on point forecasts. We experimentally offer 10 quantile heads but they have not been calibrated after pretraining.
-- It requires the context to be contiguous (i.e. no "holes"), and the context and the horizon to be of the same frequency.
 
 ## Benchmarks