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

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
Main Authors: Lkhagvadorj Munkhdalai, Tsendsuren Munkhdalai, Kwang Ho Park, Tsatsral Amarbayasgalan, Erdenebileg Erdenebaatar, Hyun Woo Park, Keun Ho Ryu
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Multivariate time series
recurrent neural network
end-to-end learning
Online Access:https://ieeexplore.ieee.org/document/8767995/

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