An End-to-End Adaptive Input Selection With Dynamic Weights for Forecasting Multivariate Time Series

A multivariate time series forecasting is critical in many applications, such as signal processing, finance, air quality forecasting, and pattern recognition. In particular, determining the most relevant variables and proper lag length from multivariate time series is challenging. This paper propose...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.99099-99114
Main Authors: Munkhdalai, Lkhagvadorj, Munkhdalai, Tsendsuren, Kwang Ho Park, Amarbayasgalan, Tsatsral, Erdenebaatar, Erdenebileg, Hyun Woo Park, Keun Ho Ryu
Format: Article
Language:English
Subjects:
Air quality
Datasets
Encoding
end-to-end learning
Forecasting
Input variables
Logic gates
Machine learning
Mathematical models
Multivariate analysis
Multivariate time series
Neural networks
Pattern recognition
recurrent neural network
Recurrent neural networks
Signal processing
Signal quality
Time series
Time series analysis
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Summary:A multivariate time series forecasting is critical in many applications, such as signal processing, finance, air quality forecasting, and pattern recognition. In particular, determining the most relevant variables and proper lag length from multivariate time series is challenging. This paper proposes an end-to-end recurrent neural network framework equipped with an adaptive input selection mechanism to improve the prediction performance for multivariate time series forecasting. The proposed model, named AIS-RNN, consists of two main components: the first neural network learns to generate context-dependent importance weights to dynamically select the input. The selected input is then fed into the second module for predicting the target variable. The experimental results show that our proposed end-to-end approach outperforms machine learning-based baselines on several public benchmark datasets. The AIS-LSTM model achieves higher performance on a public M3 dataset than the M3-specialized models. Furthermore, the AIS-RNN gives a beneficial advantage to interpret variable importance.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2930069