modelradar

Name	modelradar JSON
Version	0.1.0 JSON
	download
home_page	None
Summary	Aspect-based Forecasting Accuracy
upload_time	2024-12-21 17:48:47
maintainer	None
docs_url	None
author	None
requires_python	>=3.9
license	None
keywords	data science evaluation forecasting machine learning time series
VCS
bugtrack_url
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Travis-CI	No Travis.
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            # 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

vcerqueira@fe.up.pt
            

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