High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes
Authors: David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show on several realworld datasets that our method provides significant accuracy improvements over state-of-the-art baselines and perform an ablation study analyzing the contributions of the different components of our model. |
| Researcher Affiliation | Collaboration | David Salinas Naverlabs david.salinas@naverlabs.com Michael Bohlke-Schneider Amazon Research bohlkem@amazon.com Laurent Callot Amazon Research lcallot@amazon.com Roberto Medico Ghent University roberto.medico91@gmail.com Jan Gasthaus Amazon Research gasthaus@amazon.com |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code to perform the evaluations of our methods and different baselines is available at https://github.com/mbohlkeschneider/gluonts/tree/mv_release. |
| Open Datasets | Yes | The following publicly-available datasets are used to compare the accuracy of different multivariate forecasting models. Exchange rate: daily exchange rate between 8 currencies as used in [16] Solar: hourly photo-voltaic production of 137 stations in Alabama State used in [16] Electricity: hourly time series of the electricity consumption of 370 customers [6] Traffic: hourly occupancy rate, between 0 and 1, of 963 San Francisco car lanes [6] Taxi: spatio-temporal traffic time series of New York taxi rides [31] taken at 1214 locations every 30 minutes in the months of January 2015 (training set) and January 2016 (test set) Wikipedia: daily page views of 2000 Wikipedia pages used in [11] |
| Dataset Splits | No | The paper mentions splitting data into training and test sets but does not explicitly describe a separate validation split or how it was used (e.g., for hyperparameter tuning) with specific details like percentages or counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | Yes | GARCH, a multivariate conditional heteroskedasticity model proposed by [34] with implementation from [12]. More details about these methods can be found in the supplement. [12] Alexios Ghalanos. rmgarch: Multivariate GARCH models., 2019. R package version 1.3-6. |
| Experiment Setup | Yes | In all our low-rank experiments we use r = 10. we use B = 20. we fix the context length hyperparameter T to τ. The number of past observations m used to estimate the empirical CDFs is an hyperparameter and left constant in our experiments with m = 100. For evaluation, we generate 400 samples from each model. |