A tensor-based deep LSTM forecasting model capturing the intrinsic connection in multivariate time series

Multivariate time series forecasting has many practical applications in a variety of domains such as commerce, weather, environment, and transportation. There exist so many methods developed for multivariate time series forecasting. However, most forecasting methods do not focus on the intrinsic con...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2023-06, Vol.53 (12), p.15873-15888
Main Authors: Fu, Zijun, Wu, Yongming, Liu, Xiaoxuan
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Datasets
Domains
Forecasting
Machines
Manufacturing
Mathematical models
Mechanical Engineering
Modules
Multivariate analysis
Neural networks
Optimization
Optimization algorithms
Processes
Tensors
Time series
Variables
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Summary:Multivariate time series forecasting has many practical applications in a variety of domains such as commerce, weather, environment, and transportation. There exist so many methods developed for multivariate time series forecasting. However, most forecasting methods do not focus on the intrinsic connections that exist between the various variables in a multivariate time series. These connections can negatively affect the accuracy of multivariate time series forecasting. In this paper, a new deep long- and short-term memory neural network forecasting model named TDLSTM-LS is proposed to solve this problem. In this model, tensor processing with TOS optimization module, SLSQP optimization-based LSTM-LS module, and deep LSTM module constructed based on linking gates are applied to retain, strengthen and convey the intrinsic connections among multiple variables. Experiments on datasets from five different domains show that the evaluation metrics of the proposed model (TDLSTM-LS) outperform all other state-of-the-art baseline methods.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-022-04229-1