Timer-XL: Long-Context Transformers for Unified Time Series Forecasting

• URL: http://arxiv.org/abs/2410.04803v3
• Date: Wed, 18 Dec 2024 08:12:18 GMT
• Title: Timer-XL: Long-Context Transformers for Unified Time Series Forecasting
• Authors: Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, Mingsheng Long,
• Abstract summary: We present Timer-XL, a generative Transformer for unified time series forecasting. Timer-XL achieves state-of-the-art performance across challenging forecasting benchmarks through a unified approach.
• Score: 67.83502953961505
• License:
• Abstract: We present Timer-XL, a generative Transformer for unified time series forecasting. To uniformly predict 1D and 2D time series, we generalize next token prediction, predominantly adopted for causal generation of 1D sequences, to multivariate next token prediction. The proposed paradigm uniformly formulates various forecasting scenarios as a long-context generation problem. We opt for the generative Transformer, which can capture global-range and causal dependencies while providing contextual flexibility, to implement unified forecasting on univariate series characterized by non-stationarity, multivariate time series with complicated dynamics and correlations, and covariate-informed contexts that include both endogenous and exogenous time series. Technically, we propose a universal TimeAttention to facilitate generative Transformers on multiple time series, which can effectively capture fine-grained intra- and inter-series dependencies of flattened time series tokens (patches), and is further enhanced by deftly designed position embeddings for the temporal and variable dimensions. Timer-XL achieves state-of-the-art performance across challenging forecasting benchmarks through a unified approach. Based on large-scale pre-training, Timer-XL also demonstrates notable zero-shot performance, making it a promising architecture for large time series models.

Related papers

• PRformer: Pyramidal Recurrent Transformer for Multivariate Time Series Forecasting [82.03373838627606]
  Self-attention mechanism in Transformer architecture requires positional embeddings to encode temporal order in time series prediction. We argue that this reliance on positional embeddings restricts the Transformer's ability to effectively represent temporal sequences. We present a model integrating PRE with a standard Transformer encoder, demonstrating state-of-the-art performance on various real-world datasets.
  arXiv  Detail & Related papers  (2024-08-20T01:56:07Z)
• TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables [75.83318701911274]
  TimeXer ingests external information to enhance the forecasting of endogenous variables. TimeXer achieves consistent state-of-the-art performance on twelve real-world forecasting benchmarks.
  arXiv  Detail & Related papers  (2024-02-29T11:54:35Z)
• 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)
• MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting [19.573651104129443]
  Long-term time series forecasting plays an important role in various real-world scenarios. Recent deep learning methods for long-term series forecasting tend to capture the intricate patterns of time series by decomposition-based or sampling-based methods. We propose a novel deep learning network architecture, named Multi-resolution Periodic Pattern Network (MPPN), for long-term series forecasting.
  arXiv  Detail & Related papers  (2023-06-12T07:00:37Z)
• Stecformer: Spatio-temporal Encoding Cascaded Transformer for Multivariate Long-term Time Series Forecasting [11.021398675773055]
  We propose a complete solution to address problems in terms of feature extraction and target prediction. For extraction, we design an efficient-temporal encoding extractor including a semi-adaptive graph to acquire sufficient-temporal information. For prediction, we propose a Cascaded De Predictor (CDP) to strengthen the correlation between different intervals.
  arXiv  Detail & Related papers  (2023-05-25T13:00:46Z)
• Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement [51.55157852647306]
  Time series forecasting has been a widely explored task of great importance in many applications. It is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. We propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder equipped with diffusion, denoise, and disentanglement.
  arXiv  Detail & Related papers  (2023-01-08T12:20:46Z)
• W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting [7.075125892721573]
  We build a transformer model for non-stationary time series using wavelet-based transformer encoder architecture. We evaluate our framework on several publicly available benchmark time series datasets from various domains.
  arXiv  Detail & Related papers  (2022-09-08T17:39:38Z)

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.