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Found 5 papers, 1 papers with code
Breaking Boundaries: Balancing Performance and Robustness in Deep Wireless Traffic Forecasting
Our optimal model can retain up to $92. 02\%$ the performance of the original forecasting model in terms of Mean Squared Error (MSE) on clean data, while being more robust than the standard adversarially trained models on perturbed data.
Multivariate Time Series Anomaly Detection: Fancy Algorithms and Flawed Evaluation Methodology
The main objective of this work is to stimulate more effort towards important aspects of the research such as data, experiment design, evaluation methodology and result interpretability, instead of putting the highest weight on the design of increasingly more complex and "fancier" algorithms.
A Generic Approach to Integrating Time into Spatial-Temporal Forecasting via Conditional Neural Fields
Self-awareness is the key capability of autonomous systems, e. g., autonomous driving network, which relies on highly efficient time series forecasting algorithm to enable the system to reason about the future state of the environment, as well as its effect on the system behavior as time progresses.
Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection
Generally, unsupervised anomaly detection algorithms gain more popularity than the supervised ones, due to the fact that labeling KPIs is extremely time- and resource-consuming, and error-prone.
Anomalous Communications Detection in IoT Networks Using Sparse Autoencoders
A set of sparse autoencoders is then trained to learn the profile of the legitimate communications generated by an experimental smart home network.
