  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.
