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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary: The paper presents an extension of gated auto encoders to time-series data. The main idea is to use a gated auto encoder to model the time series in an autoregressive manner; predicting x_{t+1} from x_t using a gated autoencoder whose mapping unit values are initialised using a pair of contiguous datapoints. The paper introduces two interesting refinements: predictive training, and higher order relational features. Predictive training is a training criterion suitable for time series data that is different from the criterion normally used for gated auto encoders. Predictive training tries to minimise the square error in predicting x_{t+1} given x_{t} and the value of the mapping units that optimally predict x_{t} given x_{t-1}.