KairosHope: A Next-Generation Time-Series Foundation Model for Specialized Classification via Dual-Memory Architecture

Problem
This paper addresses the limitations of existing Time Series Foundation Models (TSFMs) in adapting to specialized classification tasks, particularly in the context of computational inefficiencies associated with standard attention mechanisms and the neglect of classical statistical methodologies. The authors present KairosHope as a solution to these challenges, emphasizing its potential for improved performance in domains requiring precise temporal analysis. Notably, this work is a preprint and has not yet undergone peer review.

Method
KairosHope introduces a novel architecture centered around the HOPE block, which innovatively replaces the traditional quadratic attention mechanism with a dual-memory system. This system comprises two components: Titans modules for dynamic short-term memory retention and a Continuum Memory System (CMS) for long-term historical context abstraction. To enhance the model’s inductive bias, a Hybrid Decision Head is employed, integrating deep latent representations with deterministic statistical features derived from the tsfeatures package. The model undergoes self-supervised pre-training on the extensive Monash archive, utilizing a combination of Masked Time Series Modeling (MTSM) and contrastive learning via InfoNCE. For adaptation to specialized tasks, the authors implement a Linear Probing and Full Fine-Tuning (LP-FT) protocol, which is designed to mitigate catastrophic forgetting during the transfer learning process.

Results
KairosHope demonstrates significant performance improvements over established baselines on the UCR benchmark datasets, particularly in tasks characterized by strict temporal causality, such as Human Activity Recognition (HAR) and sensor data classification. The paper reports that KairosHope achieves an average accuracy increase of approximately 5-10% over traditional TSFMs, with specific metrics indicating a reduction in classification error rates by up to 15% compared to models employing standard attention mechanisms. These results underscore the effectiveness of the dual-memory architecture and the integration of statistical features in enhancing classification performance.

Limitations
The authors acknowledge several limitations, including the reliance on the Monash archive for pre-training, which may not encompass all relevant time series characteristics. Additionally, while the dual-memory architecture shows promise, its computational overhead compared to simpler models is not fully quantified. The authors also note that the model’s performance may vary across different domains, suggesting that further empirical validation is necessary. An obvious limitation not discussed is the potential scalability of the model to extremely large datasets or real-time applications, which could pose challenges in terms of latency and resource consumption.

Why it matters
The introduction of KairosHope represents a significant advancement in the application of foundation models to time series analysis, particularly for specialized classification tasks. By effectively integrating classical statistical knowledge with modern deep learning techniques, this work paves the way for more robust and interpretable models in time series forecasting and classification. The implications extend to various domains, including finance, healthcare, and IoT, where accurate temporal analysis is critical. Future research can build upon this framework to explore further enhancements in model efficiency and adaptability.

Authors: Luis Balderas, José Alberto Rodríguez, Miguel Lastra, Antonio Arauzo-Azofra, José M. Benítez
Source: arXiv:2605.18657
URL: https://arxiv.org/abs/2605.18657v1