Reviews: Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting

This paper proposes extending temporal matrix factorization to incorporate neural network regularization for time series forecasting. The intuition is to capture global and local structure to make forecasts. The work is interesting because it bridges more classical forecasting ideas with new state of the art deep learning approaches. The ideas presented in this paper seem novel as the authors take existing building blocks for deep learning and combine them in a creative way to capture interesting structure of time series. This is in contrast to simply applying an existing deep learning approach such as seq2seq.