Table 4
Results we obtained from the analysis of forecast explanations issued for 56 products
over three months. Media Events, Media Events’ Keywords, and External Datasets
correspond to contextual information displayed for each forecast explanation.
Value
0,97
0,97
0,30
0,11
0,77
0,78
Media Events’ Keywords
0,14
0,09
0,56
External Datasets
0,41
Thismediaeventseemedunrelatedtoourusecase,butweconfirmed
that electric vehicles’ demand drives research and development of a
segment of wireless charging solutions. Finally, the most frequently
listed media event among our forecast explanations was ‘‘Number of
peopleinworksurgesagaintoanewrecordhighof32.3m’’.Weconsider
this event to relate to the economic context and be relevant to our
demandforecastingcase.
The results and examples presented above show that the current
architectureandknowledgefusionproceduresprovidecomprehensive
explanationstotheuserswhilepreservingconfidentiality.Nevertheless,
Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio