From: ~Doyup_Lee1
comment: We would like to thank to reviewer on the detailed and constructive feedbacks. As you mentioned above, a development of simpler approach that can be easily extended is important and it can give a chance to solve societally important problem, such as taxi demand forecasting. We give the answers of mentioned questions as follows.

- Yes, the shape of each sample is (I,J,T).We agree that it would be more clear to use R^{I x J x T}, so we will update the manuscript. Thank you. 

- It is true that previous approaches to utilize periodic and seasonal inputs since it is a conventional statistical approach. However, in the TGNet setting, seasonality and periodicity are captured in the temporal guided embedding, since week of day, holiday information is used in such embedding. In other words, typical monday 10 am pattern should be embedded in the temporal guided embedding. Based on this intuition, we decided not to feed seasonal or periodic input patterns in our setting, and let FCN to figure out what to do with this temporal information. Note that figuring out the right period and seasonality by looking ad partial ACF is often time consuming, which shows the TGNet’s another advantage over the existing approach.

- We think it is important and salient question about the motivation of temporal guided embedding. Previous approaches tried to use a day ago, a week ago data to forecast. However, the model do not know the temporal information of forecasting target. Then, it is a kind of ‘pattern matching’ without a temporal context. The model is trained to infer the target output from input data patterns only. We think that prior knowledge about temporal context is important for human to naturally predict future taxi demand. That is, “when is the day-of-week of target? Monday? Or Weekend?” or “when is the time-of-day? 7 p.m? 2 a.m?” can be important to forecast. With these information, we can predict future demand, combining prior knowledge about the temporal context and recent pattern dynamics.

The main reason that previous approaches use periodic and seasonal input is to refer recurrent patterns in taxi demand directly. After then, they learn how to use the patterns of input to predict future demand. Temporal guided embedding replace these approach and make prior knowledge about temporal information. “Then FCN learns to extract hidden representation with prior knowledge about periodic and seasonal patterns, and predict the output.”

It can be more “simple” model in computational efficiency, because periodic and seasonal inputs increase the computation time and the number of trainable parameters. Input shape increases (I, J, total number of time steps used). 
In this paper, we propose that only guiding the temporal context can be enough to learn and capture recurrent patterns in taxi demand, showing notable performances. Of course, more previous data can be used to increase model performance in various ways. We think our approach is starting point for future researches.

- The (a), (b), and (c)  means the parts in Figure 1. (a) means the baseline model with fully convolutional networks. (b) part is temporal guided embedding. (c) part is external information. The result means that the temporal guided embedding can give notable performance gain, when it is trained and concatenated to taxi demand inputs. It guided for FCN to extract useful hidden representations to forecast, considering the temporal information, “when is the target time-of-day, day-of-week? Is it holiday or not?”. We will mentioned exactly what is the (a), (b), and (c) in Table 1 in our paper.

- We used NYC-taxi dataset and 40 days/20 days for training/testing in order to fair comparison of our model with existing studies (All of existing experiments are done in this setting). In the longer version of our study, we also identified the effectiveness of temporal guided embedding and our model structure with 4 months/2 months as train/test set in Seoul ride-hailing datasets from a company. We couldn’t find a study that uses much longer test period than train period in mobility forecasting domain. 

title: Answers for Questions

From: ~Neal_Jean1
comment: This paper proposes temporal guided networks (TGNets) for capturing spatiotemporal patterns and accurately predicting future taxi demand. Compared to existing approaches, which rely on complex architectures and external data sources, TGNet is a simple approach based on fully convolutional networks with temporal guided embeddings that is able to achieve state-of-the-art results. In cases where external data is available, the authors also demonstrate how TGNets can be extended to incorporate this useful side information.

The problem that this paper tackles is challenging, societally important, and relevant to the workshop. The motivation of trying to develop a simpler and more generalizable approach is compelling, but the experimental methodology and results need to be explained more clearly.

Questions:

- I don't understand the notation in line 67, X^{(t:t-T)} \in R^{3T} - shouldn't this be R^{I x J x T}?
- Section 2.2 was confusing too - why introduce the period p and season s only to say that the TGNet setting doesn't use anything besides the immediate past (if I am understanding correctly)?
- In Section 3.2, the authors explain what they mean by temporal guided embeddings (instead of using historical data, use related inputs such as time-of-day, day-of-week, holiday/not holiday). It's not clear to me that this is actually "simpler" - isn't it more realistic to believe that you will have data from a week ago than to believe that you will have access to information about, e.g., local holidays in each location? 
- I assume that the 3 TGNet columns of Table 1 (a, ab, abc) correspond to different variants of TGNet models, but these are not described anywhere in the paper? Related to the previous question, this seems important for understanding which components of the proposed approach are critical to performance.
- The study period is 1/1/15-3/1/15 (60 days) where the first 40 days are used for training and the last 20 days are used for testing. Have there been any studies (either with this approach or any other) about whether these types of models generalize across longer periods of time? E.g., it seems natural to expect cyclic (e.g., seasonal) or other (e.g., holidays) fluctuations - if we have training data for 1/1-3/1, do we learn representations that are useful for making predictions for all of 3/1-12/31?
title: From: NIPS 2018 Workshop Spatiotemporal Paper44 AnonReviewer1