Multi-scale Attention Flow for Probabilistic Time Series Forecasting
- URL: http://arxiv.org/abs/2205.07493v3
- Date: Fri, 21 Jul 2023 06:28:40 GMT
- Title: Multi-scale Attention Flow for Probabilistic Time Series Forecasting
- Authors: Shibo Feng and Chunyan Miao and Ke Xu and Jiaxiang Wu and Pengcheng Wu
and Yang Zhang and Peilin Zhao
- Abstract summary: 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.
- Score: 68.20798558048678
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The probability prediction of multivariate time series is a notoriously
challenging but practical task. On the one hand, the challenge is how to
effectively capture the cross-series correlations between interacting time
series, to achieve accurate distribution modeling. On the other hand, we should
consider how to capture the contextual information within time series more
accurately to model multivariate temporal dynamics of time series. In this
work, we proposed a novel non-autoregressive deep learning model, called
Multi-scale Attention Normalizing Flow(MANF), where we integrate multi-scale
attention and relative position information and the multivariate data
distribution is represented by the conditioned normalizing flow. Additionally,
compared with autoregressive modeling methods, our model avoids the influence
of cumulative error and does not increase the time complexity. Extensive
experiments demonstrate that our model achieves state-of-the-art performance on
many popular multivariate datasets.
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