A Transformer-based Framework for Multivariate Time Series Representation Learning

• URL: http://arxiv.org/abs/2010.02803v3
• Date: Tue, 8 Dec 2020 21:57:10 GMT
• Title: A Transformer-based Framework for Multivariate Time Series Representation Learning
• Authors: George Zerveas, Srideepika Jayaraman, Dhaval Patel, Anuradha Bhamidipaty, Carsten Eickhoff
• Abstract summary: Pre-trained models can be potentially used for downstream tasks such as regression and classification, forecasting and missing value imputation. We show that our modeling approach represents the most successful method employing unsupervised learning of multivariate time series presented to date. We demonstrate that unsupervised pre-training of our transformer models offers a substantial performance benefit over fully supervised learning.
• Score: 12.12960851087613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work we propose for the first time a transformer-based framework for unsupervised representation learning of multivariate time series. Pre-trained models can be potentially used for downstream tasks such as regression and classification, forecasting and missing value imputation. By evaluating our models on several benchmark datasets for multivariate time series regression and classification, we show that not only does our modeling approach represent the most successful method employing unsupervised learning of multivariate time series presented to date, but also that it exceeds the current state-of-the-art performance of supervised methods; it does so even when the number of training samples is very limited, while offering computational efficiency. Finally, we demonstrate that unsupervised pre-training of our transformer models offers a substantial performance benefit over fully supervised learning, even without leveraging additional unlabeled data, i.e., by reusing the same data samples through the unsupervised objective.

Related papers

• 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)
• Timer: Generative Pre-trained Transformers Are Large Time Series Models [83.03091523806668]
  This paper aims at the early development of large time series models (LTSM) During pre-training, we curate large-scale datasets with up to 1 billion time points. To meet diverse application needs, we convert forecasting, imputation, and anomaly detection of time series into a unified generative task.
  arXiv  Detail & Related papers  (2024-02-04T06:55:55Z)
• Multi-Granularity Framework for Unsupervised Representation Learning of Time Series [1.003058966910087]
  This paper proposes an unsupervised framework to realize multi-granularity representation learning for time series. Specifically, we employed a cross-granularity transformer to develop an association between fine- and coarse-grained representations. In addition, we introduced a retrieval task as an unsupervised training task to learn the multi-granularity representation of time series.
  arXiv  Detail & Related papers  (2023-12-12T13:25:32Z)
• Compatible Transformer for Irregularly Sampled Multivariate Time Series [75.79309862085303]
  We propose a transformer-based encoder to achieve comprehensive temporal-interaction feature learning for each individual sample. We conduct extensive experiments on 3 real-world datasets and validate that the proposed CoFormer significantly and consistently outperforms existing methods.
  arXiv  Detail & Related papers  (2023-10-17T06:29:09Z)
• Toward a Foundation Model for Time Series Data [34.1973242428317]
  A foundation model is a machine learning model trained on a large and diverse set of data. We develop an effective time series foundation model by leveraging unlabeled samples from multiple domains.
  arXiv  Detail & Related papers  (2023-10-05T21:44:50Z)
• 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)
• Ti-MAE: Self-Supervised Masked Time Series Autoencoders [16.98069693152999]
  We propose a novel framework named Ti-MAE, in which the input time series are assumed to follow an integrate distribution. Ti-MAE randomly masks out embedded time series data and learns an autoencoder to reconstruct them at the point-level. Experiments on several public real-world datasets demonstrate that our framework of masked autoencoding could learn strong representations directly from the raw data.
  arXiv  Detail & Related papers  (2023-01-21T03:20:23Z)
• Self-Distillation for Further Pre-training of Transformers [83.84227016847096]
  We propose self-distillation as a regularization for a further pre-training stage. We empirically validate the efficacy of self-distillation on a variety of benchmark datasets for image and text classification tasks.
  arXiv  Detail & Related papers  (2022-09-30T02:25:12Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• Few-Shot Forecasting of Time-Series with Heterogeneous Channels [4.635820333232681]
  We develop a model composed of permutation-invariant deep set-blocks which incorporate a temporal embedding. We show through experiments that our model provides a good generalization, outperforming baselines carried over from simpler scenarios.
  arXiv  Detail & Related papers  (2022-04-07T14:02:15Z)
• Multi-Time Attention Networks for Irregularly Sampled Time Series [18.224344440110862]
  Irregular sampling occurs in many time series modeling applications. We propose a new deep learning framework for this setting that we call Multi-Time Attention Networks. Our results show that our approach performs as well or better than a range of baseline and recently proposed models.
  arXiv  Detail & Related papers  (2021-01-25T18:57:42Z)

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.