Simplets: An efficient and universal model selection framework for time series forecasting
Time series forecasting, that predicts events through a sequence of time, has received
increasing attention in past decades. The diverse range of time series forecasting models …
increasing attention in past decades. The diverse range of time series forecasting models …
Anomaly detection and inter-sensor transfer learning on smart manufacturing datasets
Smart manufacturing systems are considered the next generation of manufacturing
applications. One important goal of the smart manufacturing system is to rapidly detect and …
applications. One important goal of the smart manufacturing system is to rapidly detect and …
Hierarchical proxy modeling for improved hpo in time series forecasting
Selecting the right set of hyperparameters is crucial in time series forecasting. The classical
temporal cross-validation framework for hyperparameter optimization (HPO) often leads to …
temporal cross-validation framework for hyperparameter optimization (HPO) often leads to …
A Demonstration of TENDS: Time Series Management System based on Model Selection
The growth in sensor technologies, IoT devices, and information systems has opened up
new opportunities for managing time series data across various domains. Despite significant …
new opportunities for managing time series data across various domains. Despite significant …
AutoXPCR: Automated multi-objective model selection for time series forecasting
Automated machine learning (AutoML) streamlines the creation of ML models, but few
specialized methods have approached the challenging domain of time series forecasting …
specialized methods have approached the challenging domain of time series forecasting …
Evaluation-Free Time-Series Forecasting Model Selection via Meta-Learning
Time-series forecasting models are invariably used in a variety of domains for crucial
decision-making. Traditionally these models are constructed by experts with considerable …
decision-making. Traditionally these models are constructed by experts with considerable …
A systematic evaluation of white-box explainable AI methods for anomaly detection in IoT systems
AN Gummadi, O Arreche, M Abdallah - Internet of Things, 2025 - Elsevier
The rapid evolution of Internet of Things (IoT) systems has created new avenues for studying
how artificial intelligence (AI) might be used to improve anomaly detection in these systems …
how artificial intelligence (AI) might be used to improve anomaly detection in these systems …
TaylorS: A Multi-Order Expansion Structure for Urban Spatio-Temporal Forecasting
J Qin, Y Jia, B Fang, Q Liao - IEEE Transactions on Knowledge …, 2025 - ieeexplore.ieee.org
Although a variety of models have been proposed for urban spatio-temporal forecasting,
most existing forecasting models are developed manually for specific tasks. By investigating …
most existing forecasting models are developed manually for specific tasks. By investigating …
XAI-IoT: An Explainable AI Framework for Enhancing Anomaly Detection in IoT Systems
AN Gummadi, JC Napier, M Abdallah - IEEE Access, 2024 - ieeexplore.ieee.org
The exponential growth of Internet of Things (IoT) systems inspires new research directions
on develo** artificial intelligence (AI) techniques for detecting anomalies in these IoT …
on develo** artificial intelligence (AI) techniques for detecting anomalies in these IoT …
SCARNet: using convolution neural network to predict time series with time-varying variance
Time series forecasting tasks are important in practical scenarios as they can be applied in
various fields such as economics, meteorology, and transportation. However, there are still …
various fields such as economics, meteorology, and transportation. However, there are still …