Dynamic Prediction Length for Time Series with Sequence to Sequence Networks

Recurrent neural networks are very popular and effective at solving difficult sequence problems such as language translation, creation of artificial music, and video prediction. New architectures, such as Sequence to Sequences networks by Sutskever et al. (2014) and Memory Networks by Sukhbaatar et al. (2015) are used to solve problems in language translation and answer questions using a large memory bank. However, these problems generally have training data with given sequence outputs (for example, a model translating a sentence from English to 1 Spanish). Because input and output sequences are known a priori for these problems, it is possible to solve them with a fixed model architecture. A fixed model architecture is effective for sequences, but there are a number of problems related to multiple time series datasets that do not have a natural sequence size.