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

Ganeshan Niroshan, Uthayasanker Thayasivam

TL;DR

The paper tackles the gap where self-supervised TS2Vec representations fail to capture deterministic components essential for long-horizon forecasting. It introduces TS2Vec-Ensemble, a hybrid framework that fuses implicit TS2Vec embeddings with explicit sinusoidal time features via horizon-adaptive weights. Through ablations on masked-signal modeling and a sinusoidal–XGBoost decomposition, the study shows MSM degrades performance while the ensemble consistently improves univariate and multivariate forecasts on ETT benchmarks. The results validate that combining learned dynamics with explicit temporal priors yields superior accuracy and stability for long-range time-series forecasting.

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.

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

TL;DR

The paper tackles the gap where self-supervised TS2Vec representations fail to capture deterministic components essential for long-horizon forecasting. It introduces TS2Vec-Ensemble, a hybrid framework that fuses implicit TS2Vec embeddings with explicit sinusoidal time features via horizon-adaptive weights. Through ablations on masked-signal modeling and a sinusoidal–XGBoost decomposition, the study shows MSM degrades performance while the ensemble consistently improves univariate and multivariate forecasts on ETT benchmarks. The results validate that combining learned dynamics with explicit temporal priors yields superior accuracy and stability for long-range time-series forecasting.

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.

Paper Structure

This paper contains 32 sections, 4 equations, 1 figure, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Research Contributions
  4. Related Work
  5. Classical and Modern Forecasting Models
  6. Unsupervised and Self-Supervised Representation Learning
  7. TS2Vec: Hierarchical Contrastive Learning
  8. Foundational Architecture: TS2Vec
  9. Encoder Architecture
  10. Contextual Consistency and Hierarchical Contrasting
  11. Dual Contrastive Loss Function
  12. Temporal Contrastive Loss
  13. Instance-wise Contrastive Loss
  14. Overall Loss
  15. Proposed Methods and Architectural Ablation
  16. ...and 17 more sections

Figures (1)

  • Figure 1: The original TS2Vec architecture with hierarchical contrastive learning. The encoder uses multi-scale dilated convolutions and contrastive objectives to learn robust representations yue2022ts2vec.