Multivariate time series forecasting is extensively studied throughout the
years with ubiquitous applications in areas such as finance, traffic,
environment, etc. Still, concerns have been raised on traditional methods for
incapable of modeling complex patterns or dependencies lying in real word data.
To address such concerns, various deep learning models, mainly Recurrent Neural
Network (RNN) based methods, are proposed. Nevertheless, capturing extremely
long-term patterns while effectively incorporating information from other
variables remains a challenge for time-series forecasting. Furthermore,
lack-of-explainability remains one serious drawback for deep neural network
models. Inspired by Memory Network proposed for solving the question-answering
task, we propose a deep learning based model named Memory Time-series network
(MTNet) for time series forecasting. MTNet consists of a large memory
component, three separate encoders, and an autoregressive component to train
jointly. Additionally, the attention mechanism designed enable MTNet to be
highly interpretable. We can easily tell which part of the historic data is
referenced the most.

提出了一种名为基于记忆的时序网络的深度学习模型，具有较强的可解释性，旨在解决传统方法无法捕捉复杂模式或在多重变量中融合信息的问题。