TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis

• URL: http://arxiv.org/abs/2402.16412v1
• Date: Mon, 26 Feb 2024 09:11:12 GMT
• Title: TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis
• Authors: Sabera Talukder and Yisong Yue and Georgia Gkioxari
• Abstract summary: We propose a simple tokenizer architecture that embeds time series data from varying domains using a discrete vectorized representation learned in a self-supervised manner. We study the efficacy of TOTEM with an extensive evaluation on 17 real world time series datasets across 3 tasks.
• Score: 32.854449155765344
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of general time series analysis has recently begun to explore unified modeling, where a common architectural backbone can be retrained on a specific task for a specific dataset. In this work, we approach unification from a complementary vantage point: unification across tasks and domains. To this end, we explore the impact of discrete, learnt, time series data representations that enable generalist, cross-domain training. Our method, TOTEM, or TOkenized Time Series EMbeddings, proposes a simple tokenizer architecture that embeds time series data from varying domains using a discrete vectorized representation learned in a self-supervised manner. TOTEM works across multiple tasks and domains with minimal to no tuning. We study the efficacy of TOTEM with an extensive evaluation on 17 real world time series datasets across 3 tasks. We evaluate both the specialist (i.e., training a model on each domain) and generalist (i.e., training a single model on many domains) settings, and show that TOTEM matches or outperforms previous best methods on several popular benchmarks. The code can be found at: https://github.com/SaberaTalukder/TOTEM.

Related papers

• GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation [90.53485251837235]
  Time series foundation models excel in zero-shot forecasting, handling diverse tasks without explicit training. GIFT-Eval is a pioneering benchmark aimed at promoting evaluation across diverse datasets. GIFT-Eval encompasses 23 datasets over 144,000 time series and 177 million data points.
  arXiv  Detail & Related papers  (2024-10-14T11:29:38Z)
• Deep Time Series Models: A Comprehensive Survey and Benchmark [74.28364194333447]
  Time series data is of great significance in real-world scenarios. Recent years have witnessed remarkable breakthroughs in the time series community. We release Time Series Library (TSLib) as a fair benchmark of deep time series models for diverse analysis tasks.
  arXiv  Detail & Related papers  (2024-07-18T08:31:55Z)
• NuwaTS: a Foundation Model Mending Every Incomplete Time Series [24.768755438620666]
  We present textbfNuwaTS, a novel framework that repurposes Pre-trained Language Models for general time series imputation. NuwaTS can be applied to impute missing data across any domain. We show that NuwaTS generalizes to other time series tasks, such as forecasting.
  arXiv  Detail & Related papers  (2024-05-24T07:59:02Z)
• Chronos: Learning the Language of Time Series [79.38691251254173]
  Chronos is a framework for pretrained probabilistic time series models. We show that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks.
  arXiv  Detail & Related papers  (2024-03-12T16:53:54Z)
• MOMENT: A Family of Open Time-series Foundation Models [19.0845213853369]
  We introduce MOMENT, a family of open-source foundation models for general-purpose time series analysis. We compile a collection of public time series, called the Time series Pile, and systematically tackle time series-specific challenges. We build on recent work to design a benchmark to evaluate time series foundation models on diverse tasks and datasets in limited supervision settings.
  arXiv  Detail & Related papers  (2024-02-06T10:48:46Z)
• 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)
• Large Pre-trained time series models for cross-domain Time series analysis tasks [20.228846068418765]
  Large Pre-trained Time-series Models (LPTM) is a novel method of adaptive segmentation that automatically identifies optimal dataset-specific segmentation strategy during pre-training. LPTM achieves superior forecasting and time-series classification results taking up to 40% less data and 50% less training time compared to state-of-art baselines.
  arXiv  Detail & Related papers  (2023-11-19T20:16:16Z)
• Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain [54.67888148566323]
  We introduce three large-scale time series forecasting datasets from the cloud operations domain. We show it is a strong zero-shot baseline and benefits from further scaling, both in model and dataset size. Accompanying these datasets and results is a suite of comprehensive benchmark results comparing classical and deep learning baselines to our pre-trained method.
  arXiv  Detail & Related papers  (2023-10-08T08:09:51Z)
• 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)

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.