# DFSeer: A Visual Analytics Approach to Facilitate Model Selection for Demand Forecasting

arXiv: https://arxiv.org/abs/2005.03244 · HTML: https://mlsanity.com/paper/2005.03244/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 8 of 8)

- [Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML](https://mlsanity.com/paper/2104.04375/) (2021)
- [Class-constrained t-SNE: Combining Data Features and Class Probabilities](https://mlsanity.com/paper/2308.13837/) (2023)
- [IMACS: Image Model Attribution Comparison Summaries](https://mlsanity.com/paper/2201.11196/) (2022)
- [Evidence-Driven Decision Support for AI Model Selection in Research Software Engineering](https://mlsanity.com/paper/2512.11984/) (2025)
- [DiagLink: A Dual-User Diagnostic Assistance System by Synergizing Experts with LLMs and Knowledge Graphs](https://mlsanity.com/paper/2601.20311/) (2026)
- [EXPRTS: Exploring and Probing the Robustness of Time Series Forecasting Models](https://mlsanity.com/paper/2403.03508/) (2024)
- [Designing forecasting software for forecast users: Empowering non-experts to create and understand their own forecasts](https://mlsanity.com/paper/2404.14575/) (2024)
- [Towards Explainable Quantum AI: Informing the Encoder Selection of Quantum Neural Networks via Visualization](https://mlsanity.com/paper/2512.14181/) (2025)

## Abstract (arXiv)

Selecting an appropriate model to forecast product demand is critical to the manufacturing industry. However, due to the data complexity, market uncertainty and users' demanding requirements for the model, it is challenging for demand analysts to select a proper model. Although existing model selection methods can reduce the manual burden to some extent, they often fail to present model performance details on individual products and reveal the potential risk of the selected model. This paper presents DFSeer, an interactive visualization system to conduct reliable model selection for demand forecasting based on the products with similar historical demand. It supports model comparison and selection with different levels of details. Besides, it shows the difference in model performance on similar products to reveal the risk of model selection and increase users' confidence in choosing a forecasting model. Two case studies and interviews with domain experts demonstrate the effectiveness and usability of DFSeer.