Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation

• URL: http://arxiv.org/abs/2501.04970v1
• Date: Thu, 09 Jan 2025 04:59:15 GMT
• Title: Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation
• Authors: HyunGi Kim, Siwon Kim, Jisoo Mok, Sungroh Yoon,
• Abstract summary: We introduce a pioneering test-time adaptation framework tailored for time series forecasting (TSF) TAFAS, the proposed approach to TSF-TTA, flexibly adapts source forecasters to continuously shifting test distributions while preserving the core semantic information learned during pre-training. The novel utilization of partially-observed ground truth and gated calibration module enables proactive, robust, and model-agnostic adaptation of source forecasters.
• Score: 39.7344214193566
• License:
• Abstract: Deep Neural Networks have spearheaded remarkable advancements in time series forecasting (TSF), one of the major tasks in time series modeling. Nonetheless, the non-stationarity of time series undermines the reliability of pre-trained source time series forecasters in mission-critical deployment settings. In this study, we introduce a pioneering test-time adaptation framework tailored for TSF (TSF-TTA). TAFAS, the proposed approach to TSF-TTA, flexibly adapts source forecasters to continuously shifting test distributions while preserving the core semantic information learned during pre-training. The novel utilization of partially-observed ground truth and gated calibration module enables proactive, robust, and model-agnostic adaptation of source forecasters. Experiments on diverse benchmark datasets and cutting-edge architectures demonstrate the efficacy and generality of TAFAS, especially in long-term forecasting scenarios that suffer from significant distribution shifts. The code is available at https://github.com/kimanki/TAFAS.

Related papers

• Sundial: A Family of Highly Capable Time Series Foundation Models [64.6322079384575]
  We introduce Sundial, a family of native, flexible, and scalable time series foundation models. Our model is pre-trained without specifying any prior distribution and can generate multiple probable predictions. By mitigating mode collapse through TimeFlow Loss, we pre-train a family of Sundial models on TimeBench, which exhibit unprecedented model capacity and generalization performance.
  arXiv  Detail & Related papers  (2025-02-02T14:52:50Z)
• Is Precise Recovery Necessary? A Task-Oriented Imputation Approach for Time Series Forecasting on Variable Subset [27.180618587832463]
  We propose Task-Oriented Imputation for Variable Subset Forecasting (TOI-VSF) for time series forecasting. TOI-VSF incorporates a self-supervised imputation module, agnostic to the forecasting model, designed to fill in missing variables. Extensive experiments across four datasets demonstrate the superiority of TOI-VSF, outperforming baseline methods by $15%$ on average.
  arXiv  Detail & Related papers  (2024-11-15T04:00:54Z)
• FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularities [17.164913785452367]
  We propose FlexTSF, a universal time series forecasting model that possesses better generalization and supports both regular and irregular time series. Experiments on 12 datasets show that FlexTSF outperforms state-of-the-art forecasting models respectively designed for regular and irregular time series.
  arXiv  Detail & Related papers  (2024-10-30T16:14:09Z)
• Adapting to Length Shift: FlexiLength Network for Trajectory Prediction [53.637837706712794]
  Trajectory prediction plays an important role in various applications, including autonomous driving, robotics, and scene understanding. Existing approaches mainly focus on developing compact neural networks to increase prediction precision on public datasets, typically employing a standardized input duration. We introduce a general and effective framework, the FlexiLength Network (FLN), to enhance the robustness of existing trajectory prediction against varying observation periods.
  arXiv  Detail & Related papers  (2024-03-31T17:18:57Z)
• 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)
• FreDF: Learning to Forecast in Frequency Domain [56.24773675942897]
  Time series modeling is uniquely challenged by the presence of autocorrelation in both historical and label sequences. We introduce the Frequency-enhanced Direct Forecast (FreDF) which bypasses the complexity of label autocorrelation by learning to forecast in the frequency domain.
  arXiv  Detail & Related papers  (2024-02-04T08:23:41Z)
• FrAug: Frequency Domain Augmentation for Time Series Forecasting [6.508992154478217]
  Data augmentation (DA) has become a de facto solution to expand training data size for deep learning. This paper proposes simple yet effective frequency domain augmentation techniques that ensure the semantic consistency of augmented data-label pairs in forecasting. Our results show that FrAug can boost the forecasting accuracy of TSF models in most cases.
  arXiv  Detail & Related papers  (2023-02-18T11:25:42Z)
• Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting [41.206310481507565]
  We propose a novel framework temporal conditional variational autoencoder (TCVAE) to model the dynamic distributional dependencies over time. The TCVAE infers the dependencies as a temporal conditional distribution to leverage latent variables. We show the TCVAE's superior robustness and effectiveness over the state-of-the-art MTS forecasting baselines.
  arXiv  Detail & Related papers  (2022-09-01T10:06:22Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series [20.958959332978726]
  SAF integrates a self-adaptation stage prior to forecasting based on backcasting' Our method enables efficient adaptation of encoded representations to evolving distributions, leading to superior generalization. On synthetic and real-world datasets in domains where time-series data are known to be notoriously non-stationary, such as healthcare and finance, we demonstrate a significant benefit of SAF.
  arXiv  Detail & Related papers  (2022-02-04T21:54:10Z)

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.