WSEAS Transactions on Information Science and Applications
Print ISSN: 1790-0832, E-ISSN: 2224-3402
Volume 23, 2026
ELM meets ASP – Automatic Rule Discovery for Real-Time Long-Term Time-Series Forecasting using ELM Auto-Encoders and Rule-Aware ELM Predictors
Authors: , ,
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Abstract: This paper presents a fully analytic, neuro-symbolic forecasting pipeline that automatically extracts
symbolic rules from multivariate time-series and embeds them as differentiable constraints in an Extreme Learning
Machine (ELM) predictor. An ELM-based auto-encoder (ELM-AE) first learns latent descriptors in closed form;
these descriptors are discretised and mined for frequent temporal patterns. Association-rule and inductive-logic
discovery yield a library of Answer Set Programming (ASP) clauses. The clauses are re-encoded as hinge-loss
penalties inside a second closed-form ELM forecaster (ELM-F), yielding long-horizon predictions that are both
accurate and rule-consistent. The entire system trains rapidly on commodity hardware and runs in real time
on microcontrollers, making it attractive for safety-critical digital-twin applications such as those for battery
management systems. Beyond its novel algorithmic contributions, this paper also provides a hands-on tutorial
on applying analytic ELM methods and symbolic rule integration to practical forecasting problems.
Keywords:
Extreme Learning Machine, Answer Set Programming, ELM-based Autoencoder, Automatic Rule
Discovery, Neuro-Symbolic Learning, Time Series Forecasting
Pages: 276-291
DOI: 10.37394/23209.2026.23.21