Multi-scale Attention Flow for Probabilistic Time Series Forecasting
This addresses the practical need for accurate distribution modeling in multivariate time series forecasting, with incremental improvements over existing methods.
The paper tackles the challenging problem of probabilistic multivariate time series forecasting by proposing a non-autoregressive deep learning model that integrates multi-scale attention and normalizing flows to capture cross-series correlations and temporal dynamics, achieving state-of-the-art performance on popular datasets.
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.