# Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lens

arXiv: https://arxiv.org/abs/2406.12908 · HTML: https://mlsanity.com/paper/2406.12908/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [One pixel attack for fooling deep neural networks](https://mlsanity.com/paper/1710.08864/) (2017)
- [Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders](https://mlsanity.com/paper/1902.00450/) (2019)
- [Causal Inference for Time series Analysis: Problems, Methods and Evaluation](https://mlsanity.com/paper/2102.05829/) (2021)
- [A Neural Mean Embedding Approach for Back-door and Front-door Adjustment](https://mlsanity.com/paper/2210.06610/) (2022)
- [Rating Sentiment Analysis Systems for Bias through a Causal Lens](https://mlsanity.com/paper/2302.02038/) (2023)
- [Advances in Automatically Rating the Trustworthiness of Text Processing Services](https://mlsanity.com/paper/2302.09079/) (2023)
- [The Effect of Human v/s Synthetic Test Data and Round-tripping on Assessment of Sentiment Analysis Systems for Bias](https://mlsanity.com/paper/2401.12985/) (2024)
- [Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders](https://mlsanity.com/paper/2010.09246/) (2020)

## Built on by (most cited first, 2 of 2)

- [Holistic Explainable AI (H-XAI): Extending Transparency Beyond Developers in AI-Driven Decision Making](https://mlsanity.com/paper/2508.05792/) (2025)
- [On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating](https://mlsanity.com/paper/2508.20437/) (2025)

## Abstract (arXiv)

AI systems are notorious for their fragility; minor input changes can potentially cause major output swings. When such systems are deployed in critical areas like finance, the consequences of their uncertain behavior could be severe. In this paper, we focus on multi-modal time-series forecasting, where imprecision due to noisy or incorrect data can lead to erroneous predictions, impacting stakeholders such as analysts, investors, and traders. Recently, it has been shown that beyond numeric data, graphical transformations can be used with advanced visual models to achieve better performance. In this context, we introduce a rating methodology to assess the robustness of Multi-Modal Time-Series Forecasting Models (MM-TSFM) through causal analysis, which helps us understand and quantify the isolated impact of various attributes on the forecasting accuracy of MM-TSFM. We apply our novel rating method on a variety of numeric and multi-modal forecasting models in a large experimental setup (six input settings of control and perturbations, ten data distributions, time series from six leading stocks in three industries over a year of data, and five time-series forecasters) to draw insights on robust forecasting models and the context of their strengths. Within the scope of our study, our main result is that multi-modal (numeric + visual) forecasting, which was found to be more accurate than numeric forecasting in previous studies, can also be more robust in diverse settings. Our work will help different stakeholders of time-series forecasting understand the models` behaviors along trust (robustness) and accuracy dimensions to select an appropriate model for forecasting using our rating method, leading to improved decision-making.