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Multi-Horizon Time Series Forecasting with Temporal Attention Learning

Authors:

Heng HuangAuthors Info & Claims

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2527 - 2535

https://doi.org/10.1145/3292500.3330662

Published: 25 July 2019 Publication History

Abstract

We propose a novel data-driven approach for solving multi-horizon probabilistic forecasting tasks that predicts the full distribution of a time series on future horizons. We illustrate that temporal patterns hidden in historical information play an important role in accurate forecasting of long time series. Traditional methods rely on setting up temporal dependencies manually to explore related patterns in historical data, which is unrealistic in forecasting long-term series on real-world data. Instead, we propose to explicitly learn constructing hidden patterns' representations with deep neural networks and attending to different parts of the history for forecasting the future.

In this paper, we propose an end-to-end deep-learning framework for multi-horizon time series forecasting, with temporal attention mechanisms to better capture latent patterns in historical data which are useful in predicting the future. Forecasts of multiple quantiles on multiple future horizons can be generated simultaneously based on the learned latent pattern features. We also propose a multimodal fusion mechanism which is used to combine features from different parts of the history to better represent the future. Experiment results demonstrate our approach achieves state-of-the-art performance on two large-scale forecasting datasets in different domains.

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Cited By

Zhu SZheng JMa Q(2025)MR-Transformer: Multiresolution Transformer for Multivariate Time Series PredictionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.332741636:1(1171-1183)Online publication date: Jan-2025
https://doi.org/10.1109/TNNLS.2023.3327416
Guo QFang LWang RZhang C(2025)Multivariate Time Series Forecasting Using Multiscale Recurrent Networks With Scale Attention and Cross-Scale GuidanceIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.332614036:1(540-554)Online publication date: Jan-2025
https://doi.org/10.1109/TNNLS.2023.3326140
Tian XHuang SXiao JWang HLiu Y(2025)SDVS-Net: A spatial dilated convolution and variable self-attention network for multivariate long-term time series forecastingNeurocomputing10.1016/j.neucom.2024.129148619(129148)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129148
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Index Terms

Multi-Horizon Time Series Forecasting with Temporal Attention Learning
1. Applied computing
2. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
    2. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Conferences

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2019

3305 pages

ISBN:9781450362016

DOI:10.1145/3292500

General Chairs:
Ankur Teredesai
KenSci
,
Vipin Kumar
University of Minnesota
,
Program Chairs:
Ying Li
EV Analysis Corporation
,
Rómer Rosales
LinkedIn
,
Evimaria Terzi
Boston University
,
George Karypis
University of Minnesota

Copyright © 2019 ACM.

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Publication History

Published: 25 July 2019

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KDD '19

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KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 4 - 8, 2019

AK, Anchorage, USA

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KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '25

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

Zhu SZheng JMa Q(2025)MR-Transformer: Multiresolution Transformer for Multivariate Time Series PredictionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.332741636:1(1171-1183)Online publication date: Jan-2025
https://doi.org/10.1109/TNNLS.2023.3327416
Guo QFang LWang RZhang C(2025)Multivariate Time Series Forecasting Using Multiscale Recurrent Networks With Scale Attention and Cross-Scale GuidanceIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.332614036:1(540-554)Online publication date: Jan-2025
https://doi.org/10.1109/TNNLS.2023.3326140
Tian XHuang SXiao JWang HLiu Y(2025)SDVS-Net: A spatial dilated convolution and variable self-attention network for multivariate long-term time series forecastingNeurocomputing10.1016/j.neucom.2024.129148619(129148)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129148
Yu BWang XWang H(2025)A geometric model with stochastic error for abnormal motion detection of portal crane bucket grabEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109481139(109481)Online publication date: Jan-2025
https://doi.org/10.1016/j.engappai.2024.109481
Ilbert ROdonnat AFeofanov VVirmaux APaolo GPalpanas TRedko ISalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)SAMformerProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692911(20924-20954)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692911
M. Sanjai Dr. C. Meenakshi (2024)Efficient Clothing Fashion Prediction using Machine LearningInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJETIR-1231(169-173)Online publication date: 10-Jul-2024
https://doi.org/10.48175/IJETIR-1231
Fan YTang QGuo YWei Y(2024)BiLSTM-MLAM: A Multi-Scale Time Series Prediction Model for Sensor Data Based on Bi-LSTM and Local Attention MechanismsSensors10.3390/s2412396224:12(3962)Online publication date: 19-Jun-2024
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Liu XWang W(2024)Deep Time Series Forecasting Models: A Comprehensive SurveyMathematics10.3390/math1210150412:10(1504)Online publication date: 11-May-2024
https://doi.org/10.3390/math12101504
Kummaraka USrisuradetchai P(2024)Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian ExportsForecasting10.3390/forecast60300336:3(616-636)Online publication date: 2-Aug-2024
https://doi.org/10.3390/forecast6030033
Yu JLi XYang LLi LHuang ZShen KYang XYang XXu ZZhang DDu S(2024)Deep Learning Models for PV Power Forecasting: ReviewEnergies10.3390/en1716397317:16(3973)Online publication date: 10-Aug-2024
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