Generative Pretrained Hierarchical Transformer for Time Series Forecasting
- URL: http://arxiv.org/abs/2402.16516v2
- Date: Tue, 18 Jun 2024 02:09:45 GMT
- Title: Generative Pretrained Hierarchical Transformer for Time Series Forecasting
- Authors: Zhiding Liu, Jiqian Yang, Mingyue Cheng, Yucong Luo, Zhi Li,
- Abstract summary: 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.
- Score: 3.739587363053192
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent efforts have been dedicated to enhancing time series forecasting accuracy by introducing advanced network architectures and self-supervised pretraining strategies. Nevertheless, existing approaches still exhibit two critical drawbacks. Firstly, these methods often rely on a single dataset for training, limiting the model's generalizability due to the restricted scale of the training data. Secondly, the one-step generation schema is widely followed, which necessitates a customized forecasting head and overlooks the temporal dependencies in the output series, and also leads to increased training costs under different horizon length settings. To address these issues, we propose a novel generative pretrained hierarchical transformer architecture for forecasting, named \textbf{GPHT}. There are two aspects of key designs in GPHT. On the one hand, we advocate for constructing a mixed dataset under the channel-independent assumption for pretraining our model, comprising various datasets from diverse data scenarios. This approach significantly expands the scale of training data, allowing our model to uncover commonalities in time series data and facilitating improved transfer to specific datasets. On the other hand, GPHT employs an auto-regressive forecasting approach, effectively modeling temporal dependencies in the output series. Importantly, no customized forecasting head is required, enabling \textit{a single model to forecast at arbitrary horizon settings.} 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. We make our codes publicly available\footnote{https://github.com/icantnamemyself/GPHT}.
Related papers
- Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting.
Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server.
We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
arXiv Detail & Related papers (2024-11-24T04:56:45Z) - A Mamba Foundation Model for Time Series Forecasting [13.593170999506889]
We introduce TSMamba, a linear-complexity foundation model for time series forecasting built on the Mamba architecture.
The model captures temporal dependencies through both forward and backward Mamba encoders, achieving high prediction accuracy.
It also achieves competitive or superior full-shot performance compared to task-specific prediction models.
arXiv Detail & Related papers (2024-11-05T09:34:05Z) - DAM: Towards A Foundation Model for Time Series Forecasting [0.8231118867997028]
We propose a neural model that takes randomly sampled histories and outputs an adjustable basis composition as a continuous function of time.
It involves three key components: (1) a flexible approach for using randomly sampled histories from a long-tail distribution; (2) a transformer backbone that is trained on these actively sampled histories to produce, as representational output; and (3) the basis coefficients of a continuous function of time.
arXiv Detail & Related papers (2024-07-25T08:48:07Z) - 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]
We propose a novel method of textitadaptive segmentation that automatically identifies optimal dataset-specific segmentation strategy during pre-training.
This enables LPTM to perform similar to or better than domain-specific state-of-art model when fine-tuned to different downstream time-series analysis tasks and under zero-shot settings.
arXiv Detail & Related papers (2023-11-19T20:16:16Z) - Lag-Llama: Towards Foundation Models for Probabilistic Time Series
Forecasting [54.04430089029033]
We present Lag-Llama, a general-purpose foundation model for time series forecasting based on a decoder-only transformer architecture.
Lag-Llama is pretrained on a large corpus of diverse time series data from several domains, and demonstrates strong zero-shot generalization capabilities.
When fine-tuned on relatively small fractions of such previously unseen datasets, Lag-Llama achieves state-of-the-art performance.
arXiv Detail & Related papers (2023-10-12T12:29:32Z) - 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) - 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) - Improving the Accuracy of Global Forecasting Models using Time Series
Data Augmentation [7.38079566297881]
Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown promising results in forecasting competitions and real-world applications.
We propose a novel, data augmentation based forecasting framework that is capable of improving the baseline accuracy of GFM models in less data-abundant settings.
arXiv Detail & Related papers (2020-08-06T13:52:20Z)
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.