TS2Vec-Ensemble: An Enhanced Self-Supervised Framework for Time Series Forecasting

2025-11-27

AI Summary

Existing self-supervised time series representation learning methods (e.g., TS2Vec) focus on instance discrimination and struggle to effectively capture predictive patterns such as trends and seasonality. To address this, we propose HybridTS—a hybrid forecasting framework that jointly leverages implicit dynamics learning and explicit temporal priors. Its core innovation is a dual-regression head architecture built upon a pre-trained TS2Vec encoder: one head models latent dynamic features, while the other processes hand-crafted trend and seasonal components. A prediction-horizon-adaptive, learnable weighted ensemble mechanism dynamically balances short-term variations and long-term periodic patterns. Evaluated on benchmark datasets including ETT, HybridTS significantly outperforms TS2Vec and state-of-the-art forecasting models. Results demonstrate that integrating self-supervised representations with structured temporal priors enhances both predictive accuracy and generalization across diverse forecasting scenarios.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Representation Learning for Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Self-supervised representation learning, particularly through contrastive methods like TS2Vec, has advanced the analysis of time series data. However, these models often falter in forecasting tasks because their objective functions prioritize instance discrimination over capturing the deterministic patterns, such as seasonality and trend, that are critical for accurate prediction. This paper introduces TS2Vec-Ensemble, a novel hybrid framework designed to bridge this gap. Our approach enhances the powerful, implicitly learned dynamics from a pretrained TS2Vec encoder by fusing them with explicit, engineered time features that encode periodic cycles. This fusion is achieved through a dual-model ensemble architecture, where two distinct regression heads -- one focused on learned dynamics and the other on seasonal patterns -- are combined using an adaptive weighting scheme. The ensemble weights are optimized independently for each forecast horizon, allowing the model to dynamically prioritize short-term dynamics or long-term seasonality as needed. We conduct extensive experiments on the ETT benchmark datasets for both univariate and multivariate forecasting. The results demonstrate that TS2Vec-Ensemble consistently and significantly outperforms the standard TS2Vec baseline and other state-of-the-art models, validating our hypothesis that a hybrid of learned representations and explicit temporal priors is a superior strategy for long-horizon time series forecasting.

Problem

• Enhances self-supervised learning for time series forecasting accuracy
• Integrates learned dynamics with explicit seasonal and trend features
• Optimizes ensemble weights adaptively per forecast horizon

Innovation

• Fuses learned dynamics with explicit seasonal features
• Uses dual regression heads with adaptive weighting
• Optimizes ensemble weights per forecast horizon