modelradar 0.1.1

Aspect-based Forecasting Accuracy

Model Radar 🎯

A framework for aspect-based evaluation of time series forecasting models based on Nixtla's ecosystem.

Overview

Model Radar introduces a novel aspect-based forecasting evaluation approach that goes beyond traditional aggregate metrics. Our framework enables:

• Fine-grained performance analysis across different forecasting aspects
• Better understanding of model behavior in varying conditions
• More informed model selection based on specific use case requirements

🚀 Getting Started

TBD

Prerequisites

TBD

📑 Reference

Cerqueira, V., Roque, L., & Soares, C. (2024). "Forecasting with Deep Learning: Beyond Average of Average of Average Performance." arXiv preprint arXiv:2406.16590

Check DS24 folder to reproduce the experiments published on this paper. The main repository and package contains an updated framework.

Contact

[e-mail]