Monitoring Early Warning Signs Evolution Through Time

In excessive business competition, detecting weak signals is very important to anticipate future changes and events. The process of detecting weak signals is very challenging, and many techniques were proposed to automatize this challenge but still needs the intervention of experts’ opinion. Understanding those detected signals and their evolution in time is crucial to reveal the alertness of possible future events and warnings. For this reason, this paper proposes a new algorithm to strengthen weak signals into early warning signs. The proposed algorithm aims to monitor and track weak signals’ evolution within time. The output will be a list of early warning signs and visualization to illustrate their evolution in time. Finally, to adequately understand the early warning signs obtained and enhance their semantic alertness, we used Word2Vec modeling to provide semantically similar words to these warning signs and improve their contextual alertness. We tested this algorithm on a web news dataset of 2006-2007 to detect early warning signs related to the 2008 financial crisis ahead of time. We obtained prominent results in strengthening and monitoring the evolution of early warning signs related to this crisis.