Forecasting Technological Directions in Wireless Networks and Mobile Computing via AutoML Framework

• URL: http://arxiv.org/abs/2606.27394v1
• Date: Mon, 22 Jun 2026 17:30:28 GMT
• Title: Forecasting Technological Directions in Wireless Networks and Mobile Computing via AutoML Framework
• Abstract summary: This study presents an automated pipeline for trend prediction in the wireless networks and mobile computing domain.<n>The framework is scalable, adaptive, and designed for robust trend analysis across scientific domains.<n> Experimental results demonstrated high predictive accuracy, achieving a Root Mean Square Error (RMSE) of 36.76.
• Score: 0.4204652164054227
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The exponential increase in scientific publications has driven the emergence of new trends. Accurate forecasting of these developments is essential for researchers and professionals to stay updated with advancements in the field. This study presents an automated pipeline for trend prediction in the wireless networks and mobile computing domain by integrating clustering, topic modeling, and time series analysis. The process begins with the collection of 127,820 abstracts from high-impact journals and conferences, followed by extensive preprocessing and semantic embedding using the SPECTER model. AutoCluster applies meta-learning to select the most suitable clustering algorithm based on the dataset meta-features, ensuring semantically coherent groupings. AutoTopicModeling then employs a successive halving strategy to identify the best-performing topic model per cluster, followed by LLM-assisted topic labeling and optional label generalization. Finally, AutoTrendAnalysis transforms topic-labeled data into time series and applies forecasting models -ARIMA, STL, Prophet, or LSTM - to predict future topic popularity. Topics are classified as strong, weak, or noise signals based on forecast trajectories, offering interpretable insights into emerging and declining research themes. The framework is scalable, adaptive, and designed for robust trend analysis across scientific domains. Experimental results demonstrated high predictive accuracy, achieving a Root Mean Square Error (RMSE) of 36.76.

Related papers

• A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Baselines [19.152026148047536]
  We take a step back and analyse the benchmark datasets via classical time series methods to uncover why spatially-unaware linear models pose a stronger competitor than previously reported.<n>Our statistical analysis provides a toolset for identifying significant spatial and temporal correlations.<n>By applying the results of our analysis to a simple hybrid model, we show how our methodology can lead to novel ways of developing GNN models.
  arXiv  Detail & Related papers  (2026-08-21T11:08:17Z)
• AutoCluster, AutoTopicModeling, AutoTrendAnalysis: A Complete AutoML Pipeline for Predicting Emerging Trends [0.2333246928962179]
  This paper presents a trend prediction framework based on Automated Machine Learning (AutoML)<n>The proposed system offers a scalable and user-friendly solution suitable for real-time applications and stakeholders with limited machine learning expertise.<n> Experimental results demonstrate that the proposed system's best trial achieves a final RMSE of 7.099, indicating high predictive accuracy.
  arXiv  Detail & Related papers  (2026-06-23T18:35:20Z)
• VESTA: Visual Exploration with Statistical Tool Agents [56.76643750765825]
  We introduce VESTA: Visual Exploration with Statistical Tool Agents.<n>We show that VESTA's dynamic tool creation outperforms prior agentic pipelines.<n>We also introduce DAWN, a benchmark targeting distribution fitting and time series.
  arXiv  Detail & Related papers  (2026-05-29T21:57:37Z)
• Wireless Traffic Prediction with Large Language Model [54.07581399989292]
  TIDES is a novel framework that captures spatial-temporal correlations for wireless traffic prediction.<n> TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead.<n>Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.
  arXiv  Detail & Related papers  (2025-12-19T04:47:40Z)
• Extreme value forecasting using relevance-based data augmentation with deep learning models [3.503370263836711]
  In this study, we present a data augmentation framework for extreme value forecasting.<n>We use deep learning models in combination with data augmentation models such as GANs and synthetic minority oversampling technique (SMOTE)<n>Our results indicate that the SMOTE-based strategy consistently demonstrated superior adaptability, leading to improved performance across both short and long-horizon forecasts.
  arXiv  Detail & Related papers  (2025-10-02T06:10:27Z)
• Automating Data-Driven Modeling and Analysis for Engineering Applications using Large Language Model Agents [3.344730946122235]
  We propose an innovative pipeline utilizing Large Language Model (LLM) agents to automate data-driven modeling and analysis.<n>We evaluate two LLM-agent frameworks: a multi-agent system featuring specialized collaborative agents, and a single-agent system based on the Reasoning and Acting (ReAct) paradigm.
  arXiv  Detail & Related papers  (2025-10-01T19:28:35Z)
• VSFormer: Value and Shape-Aware Transformer with Prior-Enhanced Self-Attention for Multivariate Time Series Classification [47.92529531621406]
  We propose a novel method, VSFormer, that incorporates both discriminative patterns (shape) and numerical information (value)<n>In addition, we extract class-specific prior information derived from supervised information to enrich the positional encoding.<n>Extensive experiments on all 30 UEA archived datasets demonstrate the superior performance of our method compared to SOTA models.
  arXiv  Detail & Related papers  (2024-12-21T07:31:22Z)
• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
  We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm. By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
  arXiv  Detail & Related papers  (2024-06-23T21:28:50Z)
• An Adaptive Framework for Generalizing Network Traffic Prediction towards Uncertain Environments [51.99765487172328]
  We have developed a new framework using time-series analysis for dynamically assigning mobile network traffic prediction models. Our framework employs learned behaviors, outperforming any single model with over a 50% improvement relative to current studies.
  arXiv  Detail & Related papers  (2023-11-30T18:58:38Z)
• A Clustering-aided Ensemble Method for Predicting Ridesourcing Demand in Chicago [0.0]
  This study proposes a Clustering-aided Ensemble Method (CEM) to forecast the zone-to-zone travel demand for ridesourcing services. We implement and test the proposed methodology by using the ridesourcing-trip data in Chicago.
  arXiv  Detail & Related papers  (2021-09-08T04:58:29Z)
• Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting [63.78344280962136]
  Using a public and a novel proprietary data set of commercial data, this research shows that the proposed system enables analysts to both cluster their user base and plan demand at a granular level. This work seeks to introduce TDA-based clustering of time series and clusterwise regression with matrix factorization methods as viable tools for the practitioner.
  arXiv  Detail & Related papers  (2020-09-08T12:10:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.