One-Shot Price Forecasting with Covariate-Guided Experts under Privacy Constraints

• URL: http://arxiv.org/abs/2601.11977v1
• Date: Sat, 17 Jan 2026 09:13:57 GMT
• Title: One-Shot Price Forecasting with Covariate-Guided Experts under Privacy Constraints
• Authors: Ren He, Yinliang Xu, Jinfeng Wang, Jeremy Watson, Jian Song,
• Abstract summary: We propose a novel MoE module that augments pretrained forecasting models by injecting a sparse mixture-of-experts layer between tokenization and encoding.<n>MoE-Encoder significantly improves forecasting accuracy compared to strong baselines.<n>Our findings suggest that MoE-Encoder provides a scalable and privacy-aware extension to foundation time series models.
• Score: 10.464301005723968
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Forecasting in power systems often involves multivariate time series with complex dependencies and strict privacy constraints across regions. Traditional forecasting methods require significant expert knowledge and struggle to generalize across diverse deployment scenarios. Recent advancements in pre-trained time series models offer new opportunities, but their zero-shot performance on domain-specific tasks remains limited. To address these challenges, we propose a novel MoE Encoder module that augments pretrained forecasting models by injecting a sparse mixture-of-experts layer between tokenization and encoding. This design enables two key capabilities: (1) trans forming multivariate forecasting into an expert-guided univariate task, allowing the model to effectively capture inter-variable relations, and (2) supporting localized training and lightweight parameter sharing in federated settings where raw data cannot be exchanged. Extensive experiments on public multivariate datasets demonstrate that MoE-Encoder significantly improves forecasting accuracy compared to strong baselines. We further simulate federated environments and show that transferring only MoE-Encoder parameters allows efficient adaptation to new regions, with minimal performance degradation. Our findings suggest that MoE-Encoder provides a scalable and privacy-aware extension to foundation time series models.

Related papers

• Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols [123.73663884421272]
  Few-shot transfer has been revolutionized by stronger pre-trained models and improved adaptation algorithms.<n>We establish FEWTRANS, a comprehensive benchmark containing 10 diverse datasets.<n>By releasing FEWTRANS, we aim to provide a rigorous "ruler" to streamline reproducible advances in few-shot transfer learning research.
  arXiv  Detail & Related papers  (2026-02-28T05:41:57Z)
• DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models [15.63564280210946]
  We propose DualWeaver, a framework that adapts uni-TSFMs (Uni-TSFMs) for multivariate forecasting by using a pair of learnable, structurally symmetric surrogate series.<n>Experiments on diverse real-world datasets show that DualWeaver outperforms state-of-the-art multivariate forecasters in both accuracy and stability.
  arXiv  Detail & Related papers  (2026-02-25T16:13:12Z)
• MoHETS: Long-term Time Series Forecasting with Mixture-of-Heterogeneous-Experts [0.8292000624465587]
  Real-world time series can exhibit intricate multi-scale structures, including global trends, local periodicities, and non-stationary regimes.<n>MoHETS integrates sparse Mixture-of-Heterogeneous-Experts layers.<n>We replace parameter-heavy linear projection heads with a lightweight convolutional patch decoder.
  arXiv  Detail & Related papers  (2026-01-29T15:35:26Z)
• Adapformer: Adaptive Channel Management for Multivariate Time Series Forecasting [49.40321003932633]
  Adapformer is an advanced Transformer-based framework that merges the benefits of CI and CD methodologies through effective channel management.<n>Adapformer achieves superior performance over existing models, enhancing both predictive accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2025-11-18T16:24:05Z)
• ForecastGAN: A Decomposition-Based Adversarial Framework for Multi-Horizon Time Series Forecasting [0.5213778368155993]
  Time series forecasting is essential across domains from finance to supply chain management.<n>This paper introduces ForecastGAN, a novel decomposition based adversarial framework for multi-horizon predictions.<n>ForecastGAN consistently outperforms state-of-the-art transformer models for short-term forecasting while remaining competitive for long-term horizons.
  arXiv  Detail & Related papers  (2025-11-06T15:19:23Z)
• Chronos-2: From Univariate to Universal Forecasting [52.753731922908905]
  Chronos-2 is a pretrained model capable of handling univariate, multivariate, and covariate-informed forecasting tasks in a zero-shot manner.<n>It delivers state-of-the-art performance across three comprehensive benchmarks: fev-bench, GIFT-Eval, and Chronos Benchmark II.<n>The in-context learning capabilities of Chronos-2 establish it as a general-purpose forecasting model that can be used "as is" in real-world forecasting pipelines.
  arXiv  Detail & Related papers  (2025-10-17T17:00:53Z)
• Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction [20.1863553357121]
  Current deep learning architectures for remote sensing are fundamentally rigid.<n>We introduce the Spatial-Temporal-Spectral Unified Network (STSUN) for unified modeling.<n> STSUN can adapt to input and output data with arbitrary spatial sizes, temporal lengths, and spectral bands.<n>It unifies various dense prediction tasks and diverse semantic class predictions.
  arXiv  Detail & Related papers  (2025-05-18T07:39:17Z)
• UniSTD: Towards Unified Spatio-Temporal Learning across Diverse Disciplines [64.84631333071728]
  We introduce bfUnistage, a unified Transformer-based framework fortemporal modeling.<n>Our work demonstrates that a task-specific vision-text can build a generalizable model fortemporal learning.<n>We also introduce a temporal module to incorporate temporal dynamics explicitly.
  arXiv  Detail & Related papers  (2025-03-26T17:33:23Z)
• PFformer: A Position-Free Transformer Variant for Extreme-Adaptive Multivariate Time Series Forecasting [9.511600544581425]
  PFformer is a position-free Transformer-based model designed for single-target MTS forecasting.<n> PFformer integrates two novel embedding strategies: Enhanced Feature-based Embedding (EFE) and Auto-Encoder-based Embedding (AEE)
  arXiv  Detail & Related papers  (2025-02-27T22:21:27Z)
• Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings [0.0]
  patio-temporal forecasting is crucial in transportation, logistics, and supply chain management. We propose a dynamic, multi-modal approach that integrates the strengths of traditional forecasting methods and instruction tuning of small language models. Our framework enables on-premises customization with reduced computational and memory demands, while maintaining inference speed and data privacy/security.
  arXiv  Detail & Related papers  (2024-08-24T16:32:58Z)
• Generative Pretrained Hierarchical Transformer for Time Series Forecasting [3.739587363053192]
  We propose a novel generative pretrained hierarchical transformer architecture for forecasting, named textbfGPHT. We conduct sufficient experiments on eight datasets with mainstream self-supervised pretraining models and supervised models. The results demonstrated that GPHT surpasses the baseline models across various fine-tuning and zero/few-shot learning settings in the traditional long-term forecasting task.
  arXiv  Detail & Related papers  (2024-02-26T11:54:54Z)
• Unified Training of Universal Time Series Forecasting Transformers [104.56318980466742]
  We present a Masked-based Universal Time Series Forecasting Transformer (Moirai) Moirai is trained on our newly introduced Large-scale Open Time Series Archive (LOTSA) featuring over 27B observations across nine domains. Moirai achieves competitive or superior performance as a zero-shot forecaster when compared to full-shot models.
  arXiv  Detail & Related papers  (2024-02-04T20:00:45Z)
• TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series [57.4208255711412]
  Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS) We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-02T16:45:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.