发表机构
German University in Cairo（开罗德国大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于AutoML的趋势预测框架，通过自动聚类、主题建模和时间序列预测，从含时间属性的文本数据中提取见解，提高趋势预测准确性，减少人工，为实时应用等提供可扩展且用户友好的方案，最佳试验RMSE达7.099。
AI 中文摘要
预测新兴趋势对企业、研究人员和政策制定者至关重要，但传统方法往往缺乏可扩展性和适应性。本文提出了一个基于自动化机器学习（AutoML）的趋势预测框架，旨在从具有时间属性的文本数据集中提取见解。该系统接收带有日期字段的特定主题文本条目。管道从预处理和嵌入开始，接着是自动聚类，它使用元学习选择最优聚类算法。然后自动主题建模应用连续减半法，根据每个聚类的一致性得分确定最佳主题建模方法：潜在狄利克雷分配（LDA）、潜在语义分析（LSA）、BERTopic或非负矩阵分解（NMF）。对于趋势预测，自动趋势分析评估多个模型：Facebook Prophet、自回归积分移动平均（ARIMA）、使用Loess的季节性趋势分解（STL）和长短期记忆（LSTM），根据均方根误差（RMSE）通过连续减半或详尽比较选择最准确的。根据预测结果将主题分类为强信号、弱信号或噪声，从而识别新兴趋势。通过自动化聚类、主题建模和时间序列预测，本研究提高了趋势预测准确性，同时减少了人工工作量。该系统为实时应用和机器学习专业知识有限的利益相关者提供了一个可扩展且用户友好的解决方案。实验结果表明，该系统最佳试验的最终RMSE为7.099，表明具有较高的预测准确性。
英文摘要
Predicting emerging trends is vital for businesses, researchers, and policymakers; yet traditional approaches often lack scalability and adaptability. This paper presents a trend prediction framework based on Automated Machine Learning (AutoML), designed to extract insights from textual datasets with temporal attributes. The system ingests subject-specific textual entries accompanied by a date field. The pipeline begins with preprocessing and embedding, followed by AutoClustering, which uses meta-learning to select the optimal clustering algorithm. AutoTopicModeling then applies successive halving to identify the best topic modeling method: Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), BERTopic, or Non-negative Matrix Factorization (NMF) based on the coherence score for each cluster. For trend forecasting, AutoTrendAnalysis evaluates multiple models: Facebook Prophet, AutoRegressive Integrated Moving Average (ARIMA), Seasonal-Trend decomposition using Loess (STL), and Long Short-Term Memory (LSTM) selecting the most accurate based on Root Mean Square Error (RMSE), either through successive halving or exhaustive comparison. Topics are classified as strong signals, weak signals, or noise based on forecasting outcomes, enabling the identification of emerging trends. By automating clustering, topic modeling, and time series forecasting, this research enhances trend prediction accuracy while reducing manual effort. The proposed system offers a scalable and user-friendly solution suitable for real-time applications and stakeholders with limited machine learning expertise. Experimental results demonstrate that the proposed system's best trial achieves a final RMSE of 7.099, indicating high predictive accuracy.
CommentsConference: 2025 International Conference on Computer and Applications (ICCA)
DOI：10.1109/ICCA66035.2025.11430858
