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View Code? Open in Web Editor NEWAAAI 2020. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
AAAI 2020. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
Sorry to disturb you, i have a question about the dataset PEMS07.
The time range of PEMS07 was 5/1/2017-8/31/2017 that lasted 123 days in your paper "AAAI20-STSGCN", but i found that there were only 98 days in the dataset you offered. Should the right time range of PEMS07 be 5/1/2017-8/6/2017?
Wish your reply soon, thanks.
Hello, sorry If I missed something in the paper but it seems that the current implementation is suitable with static graph (static number of nodes) right ?
您好:
我安装了mxnet-cu101==1.5.1,在运行的时候总会出现下面的问题:
terminate called after throwing an instance of 'dmlc::Error'
terminate called recursively
what(): [10:39:57] /home/travis/build/dmlc/mxnet-distro/mxnet-build/3rdparty/mshadow/mshadow/./stream_gpu-inl.h:115: Check failed: err == CUBLAS_STATUS_SUCCESS (1 vs. 0) : Create cublas handle failed
Stack trace:
[bt] (0) /data/wangzeyu/wangzeyu_mxnet/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x4b09db) [0x7f462d2089db]
[bt] (1) /data/wangzeyu/wangzeyu_mxnet/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x25aa13b) [0x7f462f30213b]
[bt] (2) /data/wangzeyu/wangzeyu_mxnet/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x25aaf78) [0x7f462f302f78]
[bt] (3) /data/wangzeyu/wangzeyu_mxnet/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x25c5877) [0x7f462f31d877]
[bt] (4) /data/wangzeyu/wangzeyu_mxnet/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x25c5aa6) [0x7f462f31daa6]
[bt] (5) /data/wangzeyu/wangzeyu_mxnet/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x25c0a64) [0x7f462f318a64]
[bt] (6) /data/wangzeyu/wangzeyu_mxnet/bin/../lib/libstdc++.so.6(+0xc8421) [0x7f467ba11421]
[bt] (7) /lib/x86_64-linux-gnu/libpthread.so.0(+0x76ba) [0x7f46814a76ba]
[bt] (8) /lib/x86_64-linux-gnu/libc.so.6(clone+0x6d) [0x7f46811dd4dd]
已放弃 (核心已转储)
请问您之前遇到过这种问题吗?
Thank you for your opening code. In utils.py construct_adj method, I found that you fill a same matrix in the diagnoal:
for i in range(steps): adj[i * N: (i + 1) * N, i * N: (i + 1) * N] = A
However, in your paper you said "he diagonal of the adjacency matrix are the adjacency matrices of the spatial networks of three continuous time steps".
Is there some other plcae you set the two continuous time steps metrix?
data = mx.sym.Activation(
mx.sym.FullyConnected(
data,
flatten=False,
num_hidden=num_of_filters
), 'relu'
)
这里的 num_of_filters 显然不是 C (特征)维度的长度?
Hello,
I'd like to know if you tested once your model on METR-BAY and METR-LA?
From my side, your model shows incredibly bad results on the 2 datasets.
For instance, I got 12-step ahead forecasting results for METR-LA (the metric has been adjusted for loss error on 1 single value):
(MAE, MAPE, RMSE): (14.412544945659166, 34.03278628558422, 15.84779567776617)
Thanks in advance for your kindly response.
i saw one features in PEMS 03 but 08 has three features.
Hi~
thanks for your collected data, why is there 3 dimensions in pems08 and pems04 datasets, they are used to represent what?
I'm wondering what's the reason that you recursively set the input_length (time steps) in each STSGC layer to minus 2. Does that mean you set T=12 in the first layer, T=10 in the second layer, T=8 in the third layer and T=6 in the fourth layer? Is that for best predictive accuracy or for better hierarchical spatial-temporal logics? Thank you.
(more specific, line 487 in stsgcn.py)
Hi, I saw in your dataset, the average node degree is about one or two. What is the reason behind it?
Is it possible that you to provide your datasets on google drive?
We always use more pytorch, less mxnet. So i expect one version implemented by pytorch.
How to construct a graph that conforms to the actual distribution with only detector coordinates?
Is there any written code, or can you explain the idea?
thank you !
请问ASTGCN(r) 的代码是将train_r输入ASTGCN_submodule后直接输出吗?
come again~
I have run your data on my model, but the result is unbelievable. I almost get the same result with the model graph wavenet on public dataset metr-la. But on your dataset PEMS08, I get MAE: 1.57, MAPE: 3.44%, RMSE: 3.73. Your results are MAE 17.13, MAPE 10.96%, RMSE 26.80.
which is much smaller. I have checked my code, but I didn't find any problem. Do you know why? can you tell me your experiment details?
I'd appreciated if you could provide sensors' locations of PEMS, 03, 04, 07, 08
Only the relative distance between sensors is available from the csv file. How to obtain the location of sensors?
Hi, I am unable to access baidu. Is your dataset hosted anywhere else? Thank you.
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