  Demand forecasting applications have immensely benefited from the
state-of-the-art Deep Learning methods used for time series forecasting.
Traditional uni-modal models are predominantly seasonality driven which attempt
to model the demand as a function of historic sales along with information on
holidays and promotional events. However, accurate and robust sales forecasting
calls for accommodating multiple other factors, such as natural calamities,
pandemics, elections, etc., impacting the demand for products and product
categories in general. We propose a multi-modal sales forecasting network that
combines real-life events from news articles with traditional data such as
historical sales and holiday information. Further, we fuse information from
general product trends published by Google trends. Empirical results show
statistically significant improvements in the SMAPE error metric with an
average improvement of 7.37% against the existing state-of-the-art sales
forecasting techniques on a real-world supermarket dataset.
