  While ubiquitous, textual sources of information such as company reports,
social media posts, etc. are hardly included in prediction algorithms for time
series, despite the relevant information they may contain. In this work, openly
accessible daily weather reports from France and the United-Kingdom are
leveraged to predict time series of national electricity consumption, average
temperature and wind-speed with a single pipeline. Two methods of numerical
representation of text are considered, namely traditional Term Frequency -
Inverse Document Frequency (TF-IDF) as well as our own neural word embedding.
Using exclusively text, we are able to predict the aforementioned time series
with sufficient accuracy to be used to replace missing data. Furthermore the
proposed word embeddings display geometric properties relating to the behavior
of the time series and context similarity between words.
