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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 4: AAAI-22 Technical Tracks 4

CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting

February 1, 2023

Authors

Hui He

Beijing Institute of Technology


Qi Zhang

University of Technology Sydney DeepBlue Academy of Sciences


Simeng Bai

Beijing Institute of Technology


Kun Yi

Beijing Institute of Technology


Zhendong Niu

Beijing Institute of Technology University of Pittsburgh


Proceedings:

No. 4: AAAI-22 Technical Tracks 4

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Data Mining and Knowledge Management

Downloads:

Download PDF

Abstract:

Modeling complex hierarchical and grouped feature interaction in the multivariate time series data is indispensable to comprehend the data dynamics and predicting the future condition. The implicit feature interaction and high-dimensional data make multivariate forecasting very challenging. Many existing works did not put more emphasis on exploring explicit correlation among multiple time series data, and complicated models are designed to capture long- and short-range pattern with the aid of attention mechanism. In this work, we think that pre-defined graph or general learning method is difficult due to their irregular structure. Hence, we present CATN, an end-to-end model of Cross Attentive Tree-aware Network to jointly capture the inter-series correlation and intra-series temporal pattern. We first construct a tree structure to learn hierarchical and grouped correlation and design an embedding approach that can pass dynamic message to generalize implicit but interpretable cross features among multiple time series. Next in temporal aspect, we propose a multi-level dependency learning mechanism including global&local learning and cross attention mechanism, which can combine long-range dependencies, short-range dependencies as well as cross dependencies at different time steps. The extensive experiments on different datasets from real world show the effectiveness and robustness of the method we proposed when compared with existing state-of-the-art methods.

DOI:

10.1609/aaai.v36i4.20320


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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