Review of automated time series forecasting pipelines
Time series forecasting is fundamental for various use cases in different domains such as
energy systems and economics. Creating a forecasting model for a specific use case …
energy systems and economics. Creating a forecasting model for a specific use case …
Can a byte improve our bite? An analysis of digital twins in the food industry
E Henrichs, T Noack, AM Pinzon Piedrahita, MA Salem… - Sensors, 2021 - mdpi.com
The food industry faces many challenges, including the need to feed a growing population,
food loss and waste, and inefficient production systems. To cope with those challenges …
food loss and waste, and inefficient production systems. To cope with those challenges …
Forecasting Cloud Application Workloads With CloudInsight for Predictive Resource Management
Predictive cloud resource management has been widely adopted to overcome the
limitations of reactive cloud autoscaling. The predictive resource management is highly …
limitations of reactive cloud autoscaling. The predictive resource management is highly …
A machine learning-based workflow for automatic detection of anomalies in machine tools
Despite the increased sensor-based data collection in Industry 4.0, the practical use of this
data is still in its infancy. In contrast, academic literature provides several approaches to …
data is still in its infancy. In contrast, academic literature provides several approaches to …
Digital food twins combining data science and food science: system model, applications, and challenges
C Krupitzer, T Noack, C Borsum - Processes, 2022 - mdpi.com
The production of food is highly complex due to the various chemo-physical and biological
processes that must be controlled for transforming ingredients into final products. Further …
processes that must be controlled for transforming ingredients into final products. Further …
Optimizing storage assignment, order picking, and their interaction in mezzanine warehouses
In warehouses, order picking is known to be the most labor-intensive and costly task in
which the employees account for a large part of the warehouse performance. Hence, many …
which the employees account for a large part of the warehouse performance. Hence, many …
Time series forecasting for self-aware systems
Modern distributed systems and Internet-of-Things applications are governed by fast living
and changing requirements. Moreover, they have to struggle with huge amounts of data that …
and changing requirements. Moreover, they have to struggle with huge amounts of data that …
Food informatics—Review of the current state-of-the-art, revised definition, and classification into the research landscape
C Krupitzer, A Stein - Foods, 2021 - mdpi.com
Background: The increasing population of humans, changing food consumption behavior,
as well as the recent developments in the awareness for food sustainability, lead to new …
as well as the recent developments in the awareness for food sustainability, lead to new …
Telescope: An automatic feature extraction and transformation approach for time series forecasting on a level-playing field
One central problem of machine learning is the inherent limitation to predict only what has
been learned-stationarity. Any time series property that eludes stationarity poses a …
been learned-stationarity. Any time series property that eludes stationarity poses a …
An automated forecasting framework based on method recommendation for seasonal time series
Due to the fast-paced and changing demands of their users, computing systems require
autonomic resource management. To enable proactive and accurate decision-making for …
autonomic resource management. To enable proactive and accurate decision-making for …