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 …

Snow is a vital environmental parameter and dynamically responsive to climate change,
particularly in mountainous regions. Snow cover can be monitored at variable spatial scales …

Time series forecasting is a research area with applications in various domains,
nevertheless without yielding a predominant method so far. We present ForeTiS, a …

Amounts of historical data collected increase and business intelligence applicability with
automatic forecasting of time series are in high demand. While no single time series …

Many actuarial science researchers on stochastic modeling and forecasting of systematic
mortality risk use Cairns-Blake-Dowd (CBD) Model (2006) due to its ability to consider the …

Performance regression testing is a foundation of modern DevOps processes and pipelines.
Thus, the detection of change points, ie, updates or commits that cause a significant change …

In many areas of decision-making, forecasting is an essential pillar. Consequently, many
different forecasting methods have been proposed. From our experience, recently presented …

Many monitoring tools that help developers in analyzing the run-time behavior of their
applications share a common shortcoming: they require their users to have a fair amount of …

These days, we are living in a digitalized world. Both our professional and private lives are
pervaded by various IT services, which are typically operated using distributed computing …

Failure prediction is an important aspect of self-aware computing systems. Therefore, a
multitude of different approaches has been proposed in the literature over the past few …