A Functional Dynamic Boltzmann Machine
Authors: Hiroshi Kajino
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We design numerical experiments to empirically confirm the effectiveness of our solutions. The experimental results demonstrate consistent error reductions as compared to baseline methods, from which we conclude the effectiveness of F-Dy BM for functional time series prediction. The effectiveness of F-Dy BM is empirically demonstrated using five real spatiotemporal data sets. |
| Researcher Affiliation | Industry | Hiroshi Kajino IBM Research Tokyo KAJINO@jp.ibm.com |
| Pseudocode | Yes | Algorithm 1 An online learning algorithm for F-Dy BM. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | NOAA Global Surface Temperature V4.01 We use a temperature data set provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA. It contains the global temperature anomalies from January 1880 to present, resulting in the length 1638.3 Retrieved from http://www.esrl.noaa.gov/psd/ data/gridded/data.noaaglobaltemp.html on Aug. 23, 2016. Air Data is maintained by the United States Environmental Protection Agency, and we retrieved it from Air Quality System Data Mart (http://aqsdr1.epa.gov/aqsweb/aqstmp/ airdata/download_files.html) on Dec. 28, 2016. |
| Dataset Splits | Yes | To do so, we first divide the time series into training, validation, and test sets in chronological order with ratio 3 : 3 : 4. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | For all the models, we used the RBF kernel K(x, x ; γ) = exp( γ x x 2), and prepared the instances by all the combinations of the following hyperparameters: N = 25, d = 3, η[0] {2 n}23 n=19, σ2 {2 n}2 n=0, γ {2n}5 n=0 for all the models and L = 3, λ1 = 0.1, λ2 = 0.5, λ3 = 0.9 for G-Dy BM and F-Dy BM. All the models are trained by using SGD, and the learning rate is controlled by rmsprop [Tieleman and Hinton, 2012]. |