AutoCluster, AutoTopicModeling, AutoTrendAnalysis: A Complete AutoML Pipeline for Predicting Emerging Trends
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 Semant...
| Authors | Ahmed Abolfadl, Marwa Mahmoud Abla, Mervat Abu-Elkheir et al. |
|---|---|
| Primary category | cs.DL |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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