GIFT-Eval: A Benchmark for General Time Series Forecasting
Browse and submit model evaluations in LLM benchmarks
Track, rank and evaluate open LLMs and chatbots
Evaluate adversarial robustness using generative models
Convert PaddleOCR models to ONNX format
Generate and view leaderboard for LLM evaluations
Benchmark LLMs in accuracy and translation across languages
Analyze model errors with interactive pages
View LLM Performance Leaderboard
View and submit LLM benchmark evaluations
Export Hugging Face models to ONNX
Find recent high-liked Hugging Face models
View NSQL Scores for Models
GIFT-Eval is a benchmark platform designed for general time series forecasting. It provides a standardized framework to evaluate and compare the performance of various forecasting models across diverse time series datasets. The platform aims to foster research and development in time series analysis by offering a comprehensive leaderboard and analysis tools.
• Diverse Datasets: Includes a wide range of time series datasets from different domains. • Multiple Metrics: Evaluates forecasting models using various accuracy metrics. • Model Support: Compatible with popular time series forecasting models. • Leaderboard: Displays performance rankings of different models. • Open Source: Accessible for research and experimentation. • Comprehensive Documentation: Provides detailed guidelines and best practices.
What is the purpose of GIFT Eval?
GIFT-Eval is designed to provide a standardized benchmark for comparing time series forecasting models, enabling researchers and practitioners to evaluate model performance comprehensively.
How do I submit my model to GIFT Eval?
To submit your model, follow the platform's documentation to format your data and results correctly, then upload them through the provided interface.
Can I use GIFT Eval for my own datasets?
Yes, GIFT-Eval supports custom datasets. Simply format your data according to the platform's requirements and run the benchmarking process to evaluate your models.