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 …
[HTML][HTML] Towards forecasting future snow cover dynamics in the european alps—the potential of long optical remote-sensing time series
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 …
particularly in mountainous regions. Snow cover can be monitored at variable spatial scales …
[HTML][HTML] ForeTiS: A comprehensive time series forecasting framework in Python
Time series forecasting is a research area with applications in various domains,
nevertheless without yielding a predominant method so far. We present ForeTiS, a …
nevertheless without yielding a predominant method so far. We present ForeTiS, a …
Two-step meta-learning for time-series forecasting ensemble
E Vaiciukynas, P Danenas, V Kontrimas… - IEEE Access, 2021 - ieeexplore.ieee.org
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 …
automatic forecasting of time series are in high demand. While no single time series …
[HTML][HTML] A Deep learning integrated cairns-blake-dowd (CBD) sytematic mortality risk model
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 …
mortality risk use Cairns-Blake-Dowd (CBD) Model (2006) due to its ability to consider the …
Automated triage of performance change points using time series analysis and machine learning: Data challenge paper
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 …
Thus, the detection of change points, ie, updates or commits that cause a significant change …
Telescope: An Automated Hybrid Forecasting Approach on a Level-Playing Field
In many areas of decision-making, forecasting is an essential pillar. Consequently, many
different forecasting methods have been proposed. From our experience, recently presented …
different forecasting methods have been proposed. From our experience, recently presented …
Guided exploration: A method for guiding novice users in interactive memory monitoring tools
M Weninger, E Gander, H Mössenböck - Proceedings of the ACM on …, 2021 - dl.acm.org
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 …
applications share a common shortcoming: they require their users to have a fair amount of …
[HTML][HTML] Automated Hybrid Time Series Forecasting: Design, Benchmarking, and Use Cases
A Bauer - 2021 - opus.bibliothek.uni-wuerzburg.de
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 …
pervaded by various IT services, which are typically operated using distributed computing …
[HTML][HTML] A Taxonomy of Techniques for SLO Failure Prediction in Software Systems
J Grohmann, N Herbst, A Chalbani, Y Arian, N Peretz… - Computers, 2020 - mdpi.com
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 …
multitude of different approaches has been proposed in the literature over the past few …