PeMSD3: PeMSD3
In DOEM: The Distributed Online Expectation Maximization Algorithms to Solve Parameters of Poisson Mixture Models
PeMSD3 R Documentation
PeMSD3
Description
The PeMSD3 data
Usage
data("PeMSD3")
Format
A data frame with 26208 observations on the following 12 variables.
X315836a numeric vector
X315837a numeric vector
X315838a numeric vector
X315841a numeric vector
X315842a numeric vector
X315839a numeric vector
X315843a numeric vector
X315846a numeric vector
X315847a numeric vector
X317895a numeric vector
X315849a numeric vector
X315850a numeric vector
Details
It is the traffic data of Sacramento in California, the United States.
Source
Song, C., Lin, Y., Guo, S., Wan, H. Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting[C]. Proceedings of the AAAI Conference on Artificial Intelligence, 34(1), 914-921.
References
Song, C., Lin, Y., Guo, S., Wan, H. Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting[C]. Proceedings of the AAAI Conference on Artificial Intelligence, 34(1), 914-921.
Examples
data(PeMSD3)
## maybe str(PeMSD3) ; plot(PeMSD3) ...
DOEM documentation built on March 30, 2022, 9:06 a.m.
R Package Documentation
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| PeMSD3 | R Documentation |
PeMSD3
Description
The PeMSD3 data
Usage
data("PeMSD3")
Format
A data frame with 26208 observations on the following 12 variables.
X315836a numeric vector
X315837a numeric vector
X315838a numeric vector
X315841a numeric vector
X315842a numeric vector
X315839a numeric vector
X315843a numeric vector
X315846a numeric vector
X315847a numeric vector
X317895a numeric vector
X315849a numeric vector
X315850a numeric vector
Details
It is the traffic data of Sacramento in California, the United States.
Source
Song, C., Lin, Y., Guo, S., Wan, H. Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting[C]. Proceedings of the AAAI Conference on Artificial Intelligence, 34(1), 914-921.
References
Song, C., Lin, Y., Guo, S., Wan, H. Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting[C]. Proceedings of the AAAI Conference on Artificial Intelligence, 34(1), 914-921.
Examples
data(PeMSD3) ## maybe str(PeMSD3) ; plot(PeMSD3) ...
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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