We propose 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.
This code is an implementation of "An End-to-End Adaptive Input Selection With Dynamic Weights for Forecasting Multivariate Time Series" paper. You can read from: https://ieeexplore.ieee.org/abstract/document/8767995.
Cite: Munkhdalai, Lkhagvadorj, Tsendsuren Munkhdalai, Kwang Ho Park, Tsatsral Amarbayasgalan, Erdenebileg Erdenebaatar, Hyun Woo Park, and Keun Ho Ryu. "An End-to-End Adaptive Input Selection With Dynamic Weights for Forecasting Multivariate Time Series." IEEE Access 7 (2019): 99099-99114.