Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning
Authors: Mohammad Taha Bahadori, Qi (Rose) Yu, Yan Liu
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on both synthetic datasets and real application datasets to demonstrate that our method is not only significantly faster than existing methods but also achieves lower estimation error. |
| Researcher Affiliation | Academia | Mohammad Taha Bahadori Dept. of Electrical Engineering Univ. of Southern California Los Angeles, CA 90089 mohammab@usc.edu Qi (Rose) Yu Dept. of Computer Science Univ. of Southern California Los Angeles, CA 90089 qiyu@usc.edu Yan Liu Dept. of Computer Science Univ. of Southern California Los Angeles, CA 90089 yanliu.cs@usc.edu |
| Pseudocode | Yes | Algorithm 1 Greedy Low-rank Tensor Learning |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the source code for their own method, nor does it provide a direct link to a repository for their code. |
| Open Datasets | Yes | USHCN: http://www.ncdc.noaa.gov/oa/climate/research/ushcn; CCDS: http://www-bcf.usc.edu/~liu32/data/NA-1990-2002-Monthly.csv; Yelp: http://www.yelp.com/dataset_challenge; Foursquare: [17] X. Long, L. Jin, and J. Joshi. Exploring trajectory-driven local geographic topics in foursquare. In Ubi Comp, 2012. |
| Dataset Splits | Yes | For each training length setting, we repeat the experiments for 10 times and select the model parameters via 5-fold cross validation. during the training phase, we use 5-fold cross-validation. |
| Hardware Specification | Yes | We measure the run time on a machine with a 6-core 12-thread Intel Xenon 2.67GHz processor and 12GB memory. |
| Software Dependencies | Yes | For MTL-L1 , MTL-L21 [19] and MTL-LDirty, we use MALSAR Version 1.1 [27]. We use the MATLAB Kriging Toolbox6 for the classical cokriging algorithms... (footnote 6: http://globec.whoi.edu/software/kriging/V3/english.html) |
| Experiment Setup | Yes | For each training length setting, we repeat the experiments for 10 times and select the model parameters via 5-fold cross validation. We split the data along the temporal dimension into 90% training set and 10% testing set. We choose VAR(3) model and during the training phase, we use 5-fold cross-validation. For each dataset, we first normalize it by removing the trend and diving by the standard deviation. Then we randomly pick 10% of locations (or users for Foursquare) and eliminate the measurements of all variables over the whole time span. |