Lightweight Tool for Comparing Time Series Forecasting Models
Published:Dec 28, 2025 19:55
•1 min read
•r/MachineLearning
Analysis
This article describes a web application designed to simplify the comparison of time series forecasting models. The tool allows users to upload datasets, train baseline models (like linear regression, XGBoost, and Prophet), and compare their forecasts and evaluation metrics. The primary goal is to enhance transparency and reproducibility in model comparison for exploratory work and prototyping, rather than introducing novel modeling techniques. The author is seeking community feedback on the tool's usefulness, potential drawbacks, and missing features. This approach is valuable for researchers and practitioners looking for a streamlined way to evaluate different forecasting methods.
Key Takeaways
- •The tool focuses on simplifying model comparison for time series forecasting.
- •It allows users to upload data, train models, and compare forecasts and metrics.
- •The project emphasizes transparency and reproducibility in model evaluation.
Reference
“The idea is to provide a lightweight way to: - upload a time series dataset, - train a set of baseline and widely used models (e.g. linear regression with lags, XGBoost, Prophet), - compare their forecasts and evaluation metrics on the same split.”