Add results for TimesFM-2.0

app.py

@@ -30,6 +30,7 @@ summary_urls = [
     "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/moirai_small.csv",
     "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/seasonal_naive.csv",
     "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/timesfm.csv",
+    "https://raw.githubusercontent.com/autogluon/fev/refs/heads/main/benchmarks/chronos_zeroshot/results/timesfm-2.0.csv",
 ]
 
 rename_cols = {

src/about.py

@@ -32,7 +32,7 @@ For instructions on how to evaluate your model using `fev` and contribute your r
 CHRONOS_BENCHMARK = """
 ## Chronos Benchmark II results
 
-This tab contains results for various forecasting models on the 28 datasets used in Benchmark II in the publication [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815).
+This tab contains results for various forecasting models on the 27 datasets used in Benchmark II in the publication [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815).
 
 These datasets were used for zero-shot evaluation of Chronos models (i.e., Chronos models were not trained on these datasets), but some other models did include certain datasets in their training corpus.
 

src/formatting.py

@@ -15,7 +15,8 @@ MODEL_URLS = {
     "moirai_large": "Salesforce/moirai-1.1-R-large",
     "moirai_base": "Salesforce/moirai-1.1-R-base",
     "moirai_small": "Salesforce/moirai-1.1-R-small",
-    "timesfm": "google/timesfm-1.0-200m",
+    "timesfm": "google/timesfm-1.0-200m-pytorch",
+    "timesfm-2.0": "google/timesfm-2.0-500m-pytorch",
 }