updated naming

README.md

@@ -1,5 +1,5 @@
 ---
-title: MultiTS-Eval Leaderboard
+title: MUSEval Leaderboard
 emoji: 🏆
 colorFrom: pink
 colorTo: indigo
@@ -7,17 +7,17 @@ sdk: gradio
 sdk_version: 5.49.0
 app_file: app.py
 pinned: false
-short_description: Leaderboard for MultiTS-Eval Dataset
+short_description: Leaderboard for MUSEval Dataset
 license: mit
 ---
 
-# 🏆 MultiTS-Eval Leaderboard
+# 🏆 MUSEval Leaderboard
 
-Welcome to the MultiTS-Eval (Multivariate Time Series Dataset) Leaderboard! This leaderboard tracks and compares the performance of different models on multivariate time series forecasting tasks.
+Welcome to the MUSEval (Multivariate Time Series Dataset) Leaderboard! This leaderboard tracks and compares the performance of different models on multivariate time series forecasting tasks.
 
-## 📊 About MultiTS-Eval
+## 📊 About MUSEval
 
-MultiTS-Eval is a comprehensive multivariate time series dataset designed for forecasting tasks. The dataset contains multiple time series with various characteristics and complexities, making it an ideal benchmark for evaluating time series forecasting models.
+MUSEval is a comprehensive multivariate time series dataset designed for forecasting tasks. The dataset contains multiple time series with various characteristics and complexities, making it an ideal benchmark for evaluating time series forecasting models.
 
 ## 🎯 Evaluation Metrics
 
@@ -52,7 +52,7 @@ The leaderboard uses the following metrics to evaluate model performance:
 5. Click "🚀 Submit Model" to add your results
 
 ### Dataset Information
-- Visit the "📋 Dataset Info" tab for detailed information about MultiTS-Eval
+- Visit the "📋 Dataset Info" tab for detailed information about MUSEval
 - Check submission guidelines and evaluation protocols
 
 ### Statistics

app.py

@@ -1,5 +1,5 @@
 """
-Synthefy MultiTS-Eval Leaderboard - Main Gradio Application
+Synthefy MUSEval Leaderboard - Main Gradio Application
 Following GIFT-Eval import structure with custom layout
 """
 
@@ -188,7 +188,7 @@ def create_leaderboard_interface():
                     )
         
         # About section
-        with gr.Accordion("📖 About MultiTS-Eval Leaderboard", open=False, elem_id="about-accordion"):
+        with gr.Accordion("📖 About MUSEval Leaderboard", open=False, elem_id="about-accordion"):
             gr.Markdown(BENCHMARKS_TEXT, elem_classes="markdown-text", elem_id="about-text")
         
         # Citation section
@@ -208,7 +208,7 @@ def create_leaderboard_interface():
                 gr.HTML("""
                 <div style="text-align: center; padding: 20px;">
                     <h3>Submit by creating a pull request with your model's performance here:</h3>
-                    <a href='https://github.com/Synthefy/MultiTS-Eval' 
+                    <a href='https://github.com/Synthefy/MUSEval' 
                        target='_blank' 
                        style='display: inline-block; 
                               background-color: #FF6B6B; 

demo.py

@@ -1,5 +1,5 @@
 """
-MultiTS-Eval Leaderboard - Local Demo
+MUSEval Leaderboard - Local Demo
 Imports from app.py to ensure identical functionality, loads a local demo leaderboard
 """
 
@@ -29,7 +29,7 @@ demo = create_leaderboard_interface()
 
 # Launch the demo
 if __name__ == "__main__":
-    print("🎨 MultiTS-Eval Leaderboard Local Demo")
+    print("🎨 MUSEval Leaderboard Local Demo")
     print("=" * 50)
     
     try:

results/arima_submission/metadata.json

@@ -5,6 +5,6 @@
     "task": "multivariate_forecasting",
     "dataset_version": "v1.0",
     "paper_url": "https://www.wiley.com/en-us/Time+Series+Analysis%3A+Forecasting+and+Control%2C+5th+Edition-p-9781118675021",
-    "code_url": "https://github.com/Synthefy/MultiTS-Eval/blob/main/src/multieval/baselines/arima_forecast.py",
+    "code_url": "https://github.com/Synthefy/MUSEval/blob/main/src/museval/baselines/arima_forecast.py",
     "description": "AutoRegressive Integrated Moving Average (ARIMA) model for time series forecasting. Uses autoregression, differencing, and moving averages to capture trends and patterns in multivariate time series data."
 }

results/exponential_smoothing_submission/metadata.json

@@ -5,6 +5,6 @@
     "task": "multivariate_forecasting",
     "dataset_version": "v1.0",
     "paper_url": "https://www.bauer.uh.edu/gardner/Exp-Sm-1985.pdf",
-    "code_url": "https://github.com/Synthefy/MultiTS-Eval/blob/main/src/multieval/baselines/exponential_smoothing.py",
+    "code_url": "https://github.com/Synthefy/MUSEval/blob/main/src/museval/baselines/exponential_smoothing.py",
     "description": "Exponential Smoothing method for time series forecasting. Applies exponentially decreasing weights to historical observations."
 }

results/historical_inertia_submission/metadata.json

@@ -5,6 +5,6 @@
     "task": "multivariate_forecasting",
     "dataset_version": "v1.0",
     "paper_url": "https://arxiv.org/pdf/2103.16349",
-    "code_url": "https://github.com/Synthefy/MultiTS-Eval/blob/main/src/multieval/baselines/historical_inertia.py",
+    "code_url": "https://github.com/Synthefy/MUSEval/blob/main/src/museval/baselines/historical_inertia.py",
     "description": "Historical Inertia baseline that repeates the recent history as the forecast."
 }

results/linear_regression_submission/metadata.json

@@ -4,7 +4,7 @@
     "submission_date": "2025-10-10",
     "task": "multivariate_forecasting",
     "dataset_version": "v1.0",
-    "paper_url": "https://github.com/Synthefy/MultiTS-Eval",
-    "code_url": "https://github.com/Synthefy/MultiTS-Eval/blob/main/src/multieval/baselines/linear_regression.py",
+    "paper_url": "https://github.com/Synthefy/MUSEval",
+    "code_url": "https://github.com/Synthefy/MUSEval/blob/main/src/museval/baselines/linear_regression.py",
     "description": "Linear Regression model over the history used to extrapolate the future, includes correlates for multivariate."
 }

results/linear_trend_submission/metadata.json

@@ -4,6 +4,6 @@
     "submission_date": "2025-10-10",
     "task": "multivariate_forecasting",
     "dataset_version": "v1.0",
-    "code_url": "https://github.com/Synthefy/MultiTS-Eval/blob/main/src/multieval/baselines/linear_trend.py",
+    "code_url": "https://github.com/Synthefy/MUSEval/blob/main/src/museval/baselines/linear_trend.py",
     "description": "Linear Trend model for time series forecasting."
 }

results/mean_submission/metadata.json

@@ -3,6 +3,6 @@
     "submitter": "Synthefy",
     "submission_date": "2025-10-10",
     "task": "multivariate_forecasting",
-    "code_url": "https://github.com/Synthefy/MultiTS-Eval/blob/main/src/multieval/baselines/mean_forecast.py",
+    "code_url": "https://github.com/Synthefy/MUSEval/blob/main/src/museval/baselines/mean_forecast.py",
     "description": "Uses the mean of the historical values as the prediction for all future values."
 }

src/__init__.py

@@ -1,5 +1,5 @@
 """
-MultiTS-Eval Leaderboard source package
+MUSEval Leaderboard source package
 """
 
 from .load_results import (

src/about.py

@@ -1,18 +1,18 @@
 """
-Text constants for MultiTS-Eval Leaderboard
+Text constants for MUSEval Leaderboard
 """
 
 TITLE = """
 <div id="main-title" style="text-align: center;">
-    <h1 style="font-size: 30px; margin-bottom: 15px; font-weight: bold;">📊 MultiTS-Eval Leaderboard</h1>
+    <h1 style="font-size: 30px; margin-bottom: 15px; font-weight: bold;">📊 MUSEval Leaderboard</h1>
 </div>
 """
 
 INTRODUCTION_TEXT = """
 <div style="font-size: 16px; line-height: 1.6;">
-Welcome to the MultiTS-Eval Leaderboard! This leaderboard provides comprehensive evaluation results of multivariate time series forecasting. Rows are models and columns are performance metrics.
+Welcome to the MUSEval Leaderboard! This leaderboard provides comprehensive evaluation results of multivariate time series forecasting. Rows are models and columns are performance metrics.
 Use the filters below to explore results by different criteria and compare model performance across various domains and categories. For additional details on the models, click on the models to access the Model Inspector below the table.
-Metrics are explained in "About MultiTS-Eval Leaderboard" below the table. Submissions can be added at <a href="https://github.com/Synthefy/MultiTS-Eval">this github repository</a>.
+Metrics are explained in "About MUSEval Leaderboard" below the table. Submissions can be added at <a href="https://github.com/Synthefy/MUSEval">this github repository</a>.
 This leaderboard determines the best performing model for multivariate time series forecasting tasks, as measured by the lowest Mean Absolute Percentage Error (MAPE).
 High performance on these datasets provides evidence that a model can utilize historical time series relationships to make accurate predictions.
 
@@ -46,8 +46,8 @@ BENCHMARKS_TEXT = """
 ## Contact & Support
 
 For questions about the dataset or leaderboard:
-- **Issues**: Report issues on the [GitHub repository](https://github.com/Synthefy/MultiTS-Eval)
-- **Dataset**: Try the dataset yourself on [Hugging Face](https://huggingface.co/datasets/Synthefy/MultiTS-Eval)
+- **Issues**: Report issues on the [GitHub repository](https://github.com/Synthefy/MUSEval)
+- **Dataset**: Try the dataset yourself on [Hugging Face](https://huggingface.co/datasets/Synthefy/MUSEval)
 
 ## Leaderboard Information
 

src/display/css_html_js.py

@@ -1,9 +1,9 @@
 """
-CSS and styling for MultiTS-Eval Leaderboard
+CSS and styling for MUSEval Leaderboard
 """
 
 custom_css = """
-/* Custom styling for MultiTS-Eval Leaderboard */
+/* Custom styling for MUSEval Leaderboard */
 
 /* Main title styling */
 #main-title h1 {

src/display/utils.py

@@ -1,5 +1,5 @@
 """
-Display utilities and column definitions for MultiTS-Eval Leaderboard
+Display utilities and column definitions for MUSEval Leaderboard
 """
 
 from dataclasses import dataclass

src/envs.py

@@ -1,5 +1,5 @@
 """
-Environment configuration for MultiTS-Eval Leaderboard
+Environment configuration for MUSEval Leaderboard
 """
 
 import os
@@ -12,9 +12,9 @@ class API:
         print(f"Restarting space: {repo_id}")
 
 # Repository configuration
-REPO_ID = "multits-eval-leaderboard"
-QUEUE_REPO = "multits-eval-queue"
-RESULTS_REPO = "multits-eval-results"
+REPO_ID = "museval-leaderboard"
+QUEUE_REPO = "museval-queue"
+RESULTS_REPO = "museval-results"
 
 # Paths
 EVAL_REQUESTS_PATH = "eval_requests"

src/load_results.py

@@ -1,5 +1,5 @@
 """
-Data loading utilities for MultiTS-Eval Leaderboard
+Data loading utilities for MUSEval Leaderboard
 """
 
 import json

src/populate.py

@@ -1,5 +1,5 @@
 """
-Data population functions for MultiTS-Eval Leaderboard
+Data population functions for MUSEval Leaderboard
 """
 
 import pandas as pd

src/utils.py

@@ -1,5 +1,5 @@
 """
-Utility functions for MultiTS-Eval Leaderboard
+Utility functions for MUSEval Leaderboard
 """
 
 import pandas as pd