Novel AI-powered forecasting models needed as many fail in times of crisis

As adesso said right at the beginning of the Corona crisis, the stability of long-term forecasts has been completely turned upside down since the pandemic and has led to high volatility in forecasting trends and planning uncertainty for companies.

In fact, many forecasting models are failing in the current crisis situation, which is why it is high time to take active countermeasures. Where in “normal times” decision-makers derived action-guiding forecasts from the AI analysis of large amounts of data, companies are struggling with completely changed framework conditions.

Being able to predict customer behavior, the hotline or machine utilization, today has two main reasons.

• Firstly, the data situation has changed radically in terms of quantity and quality. Analysts speak of concept drifts that lead to the formation of new patterns in the data set and bring existing models closer to their expiration date. Recognizing the current radical distortions in customer behavior is the task that companies must now solve as quickly as possible. This applies, for example, to customer sales at banks, call analysis at authorities and call centers or fault forecasts for IT support or technical systems due to changed workloads.
• Secondly, this extreme imbalance in the data has rendered many forecasting models obsolete. It is therefore necessary to adapt the AI models to the changed data situation, because, despite the exceptional situation, companies can continue to make forecasts if the experts can quickly make the appropriate adjustments. To do this, the models must be retrained with the current data material and any new influencing variables must be identified and modeled. In addition, deep learning can be used to recognize new patterns, such as neural networks, and recognized patterns can in turn be used for the forecast. To be on the safe side, those responsible should also consider so-called ensemble...