We propose Significance-Offset Convolutional Neural Network, a deep convolutional network architecture for regression of multivariate asynchronous time series. The model is inspired by standard autoregressive (AR) models and gating mechanisms used in recurrent neural networks. It involves an AR-like weighting system, where the final predictor is obtained as a weighted sum of adjusted regressors, while the weights are data-dependent functions learnt through a convolutional network. The architecture was designed for applications on asynchronous time series and is evaluated on such datasets: a hedge fund proprietary dataset of over 2 million quotes for a credit derivative index, an artificially generated noisy autoregressive series and household electricity consumption dataset. The proposed architecture achieves promising results as compared to convolutional and recurrent neural networks. The code for the numerical experiments and the architecture implementation will be shared online to make the research reproducible.
The author proposed:
1. A data augmentation technique for asynchronous time series data.
2. A convolutional 'Significance' weighting neural network that assigns normalised weights to the outputs of a fully-connected autoregressive 'Offset' neural network, such that the output is a weighted average of the 'Offset' neural net.
3. An 'auxiliary' loss function.
The experiments showed that:
1. The proposed method beat VAR/CNN/ResNet/LSTM 2 synthetic asynchronous data sets, 1 real electricity meter data set and 1 real financial bid/ask data set. It's not immediately clear how hyper-parameters for the benchmark models were chosen.
2. The author observed from the experiments that the depth of the offset network has negligible effect, and concluded that the 'Significance' network has crucial impact. (I don't see how this conclusion can be made.)
3. The proposed auxiliary loss is not useful.
4. The proposed architecture is more robust to noise in the synthetic data set compared to the benchmarks, and together with LSTM, are least prone to overfitting.
Pros
- Proposed a useful way of augmenting asynchronous multivariate time series for fitting autoregressive models
- The convolutional Significance/weighting networks appears to reduce test errors (not entirely clear)
Cons
- The novelties aren't very well-justified. The 'Significance' network was described as critical to the performance, but there is no experimental result to show the sensitivity of the model's performance with respect to the architecture of the 'Significance' network. At the very least, I'd like to see what happens if the weighting was forced to be uniform while keeping the 'Offset' network and loss unchanged.
- It's entirely unclear how the train and test data was split. This may be quite important in the case of the financial data set.
- It's also unclear if model training was done on a rolling basis, which is common for time series forecasting.
- The auxiliary loss function does not appear to be very helpful, but was described as a key component in the paper.
Quality: The quality of the paper was okay. More details of the experiments should be included in the main text to help interpret the significance of the experimental results. The experiment also did not really probe the significance of the 'Significance' network even though it's claimed to be important.
Clarity: Above average. 
Originality: Mediocre. Nothing really shines. Weighted average-type architecture has been proposed many times in neural networks (e.g., attention mechanisms). 
Significance: Low. It's unclear how useful the architecture really is.