Exploring Representations and Interventions in Time Series Foundation Models

2024-09-19

AI Summary

The internal representation mechanisms of Time Series Foundation Models (TSFMs) remain poorly understood, with limited systematic investigation into inter-layer redundancy and the distribution of interpretable temporal concepts. Method: We first identify a prevalent block-wise redundancy structure across hidden layers; propose an analytical framework grounded in representation similarity and inter-layer self-similarity metrics; construct disentangled representations of temporal concepts—such as periodicity and trend—in latent space; and design concept-level latent-space steering and structured pruning strategies. Contribution/Results: Experiments show that our pruning strategy reduces inference latency by 32%; moreover, we demonstrate precise injection of periodic or trend features into signals originally lacking them, validating both the efficacy and generalizability of concept-level manipulation. This work establishes a novel paradigm for interpretable modeling and controllable editing of TSFMs.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Time series foundation models (TSFMs) promise to be powerful tools for a wide range of applications. However, their internal representations and learned concepts are still not well understood. In this study, we investigate the structure and redundancy of representations across various TSFMs, examining the self-similarity of model layers within and across different model sizes. This analysis reveals block-like redundancy in the representations, which can be utilized for informed pruning to improve inference speed and efficiency. Additionally, we explore the concepts learned by these models - such as periodicity and trends - and how these can be manipulated through latent space steering to influence model behavior. Our experiments show that steering interventions can introduce new features, e.g., adding periodicity or trends to signals that initially lacked them. These findings underscore the value of representational analysis for optimizing models and demonstrate how conceptual steering offers new possibilities for more controlled and efficient time series analysis with TSFMs.

Problem

• Understanding internal representations in time series models.
• Exploring redundancy for efficient model pruning.
• Manipulating learned concepts to influence model behavior.

Innovation

• Block-like redundancy analysis
• Latent space steering
• Representational pruning optimization