Spatio-Temporal Random Fields (STRF)
STRF estimates prediction models for spatio-temporal data.
The parameter storage is compressed by removing uninformative
parameters in a systematic way. For finding the best parameters via
maximum likelihood estimation, a separable optimization
algorithm that can be performed independently in parallel in each graph
node is implemented in STRF.
The software is released under 4-clause BSD
License.
This is the most recent implementation of the STRF model that is
presented
in
the article Spatio-temporal
random fields: compressible representation and distributed estimation
.
Quick start
The following will train a model on smartphone utilization
data:
- Download and extract the source version of STRF.
- Uncompress the source code, change to the corresponding
directory and type make to build the learner STRF and
the predictor STRFp.
- Run ./STRF --data data/APPS_CELLS_BAT_G_3600s.csv
--vertexset data/APPS_BAT_G.V --primary day,month,year --timekey
slot --vertexw 0 --spatialw 0 --maxiter 25 --omodel apps.model
--alpha 0.25 --damp 0.75 to estimate a spatial graph, train a STRF
and store the learned model to the file ./apps.model.
- The prediction accuracy of the model can be evaluated with
./STRFp
--data data/APPS_CELLS_BAT_G_3600s.csv --vertexset
data/APPS_BAT_G.V --primary day,month,year --timekey slot
--model apps.model --alpha 0.25 --damp 0.75
A detailed description of all command line options will be added
soon.
Binary Version
The binary version of STRF that can be downloaded below, requires
no additional libraries. For the CUDA accelerated version, a CUDA
capable GPU and a recent CUDA driver (tested with 331.49) are required.
The files were compiled on Ubuntu 13.04 with
g++-4.7.3-1ubuntu1
and
CUDA compilation tools, release 5.5, V5.5.0, respectively.
The CUDA version is compiled for devices with compute capabilities 1.3,
2.0, 3.0 and 3.5.
Downloads
Binary versions for windows will be added soon.
