arXiv Machine Learning June 16, 2026 By Wenjing Liu, Yujia Lei, Qu Wang

A Biased Nonnegative Block Term Tensor Decomposition Model for Dynamic QoS Prediction

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arXiv:2605. 04813v2 Announce Type: replace Abstract: With the rapid development of cloud computing and Web services, Quality of Service (QoS) has become a key criterion for service selection and recommendation.

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arXiv Machine Learning

Jul 9

A Novel Approach to Temporal QoS Estimation via Extended Kalman Filter-Incorporated Latent Feature Analysis

arXiv:2606. 23010v2 Announce Type: replace Abstract: Predicting temporal Quality of Service (QoS) data is critical for optimizing network services and rationalizing resource allocation in cloud computing and service-oriented systems.

By Ye Yuan, Song Wang, Hongxun Zhou, Ling Wang, Xin Luo

benchmarks

arXiv Machine Learning

Aug 10

Tensor Network Kernel Machines: A JAX Framework for Machine Learning and Nonlinear System Identification

arXiv:2608. 07043v1 Announce Type: cross Abstract: Developing nonlinear models that are both expressive and computationally efficient remains a challenge in machine learning and nonlinear system identification.

By Albert Saiapin, Kim Batselier

benchmarks

arXiv AI

Jun 4

An Ensembled Latent Factor Model via Differential Evolution and Gradient Descent Optimization

arXiv:2606. 04408v1 Announce Type: cross Abstract: High-dimensional and incomplete (HDI) data are prevalent in many real-world big data scenarios.

By Rui Zhang, Jinhang Liu, Wenbo Zhang

arXiv Machine Learning

Sep 4

A Two-Stage Forecasting System for CPU Workload Prediction in Private Clouds

A two-stage forecasting system is introduced for predicting CPU workload in private clouds. The model first forecasts customer service requests in Transactions Per Second (TPS) and then estimates future CPU usage from the TPS forecast, both stages using XGBoost within a cascaded architecture. Experiments on real private‑cloud traces show SMAPE below 7% for most applications, with the best case achieving an MAE of 0.7372 and an R² of 0.9185, and stable error accumulation over a 60‑step horizon.

By Ashir Javeed, Anton Borg, H{\aa}kan Grahn, Lars Lundberg, Dhyey Patel, Sogand Shirinbab

safety

arXiv Machine Learning

Jun 10

Time series forecasting from partial observations via Non-negative Matrix Factorization

arXiv:2102. 05314v2 Announce Type: replace Abstract: In modern time series problems, one aims at forecasting multiple time series with possible missing and noisy values.

By Yohann de Castro (ICJ, PSPM, CERMICS UMR 9032, ECL, IUF), Luca Mencarelli (CERMICS UMR 9032)

llms benchmarks

arXiv AI

Jul 23

Time Series Network Utilization KPI Forecasting Using Advanced AI/ML Models

arXiv:2607. 19974v1 Announce Type: cross Abstract: The rapid proliferation of data-intensive applications, cloud infrastructure, and IoT ecosystems has made proactive resource provisioning critical for maintaining optimal network performance.

By Niraj Gadhe, Kirti Bhardwaj, Moulik Jain, Shubhi Sharma, Vinay Saini

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A Novel Approach to Temporal QoS Estimation via Extended Kalman Filter-Incorporated Latent Feature Analysis

Tensor Network Kernel Machines: A JAX Framework for Machine Learning and Nonlinear System Identification

An Ensembled Latent Factor Model via Differential Evolution and Gradient Descent Optimization

A Two-Stage Forecasting System for CPU Workload Prediction in Private Clouds

Time series forecasting from partial observations via Non-negative Matrix Factorization

Time Series Network Utilization KPI Forecasting Using Advanced AI/ML Models