Learning Vector Autoregressive Models With Latent Processes
Authors: Saber Salehkaleybar, Jalal Etesami, Negar Kiyavash, Kun Zhang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results apply to both non-Gaussian and Gaussian cases, and experimental results on various synthetic and real-world datasets validate our theoretical results. |
| Researcher Affiliation | Academia | Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, USA. Department of ISE, University of Illinois at Urbana-Champaign Urbana, USA. Department of ECE, University of Illinois at Urbana-Champaign, Urbana, USA. Department of Philosophy, Carnegie Mellon University, Pittsburgh, USA. |
| Pseudocode | Yes | Algorithm 1 DTR Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | US Macroeconomic Data: We considered the following set of time series from the quarterly US macroeconomic data for the period from 31-Mar-1947 to 31-Mar-2009 collected from the St. Louis Federal Reserve Economic Database (FRED) (FRE ). |
| Dataset Splits | No | The paper does not provide specific details on training, validation, and test dataset splits, such as percentages or sample counts, that would be needed to reproduce the data partitioning. |
| Hardware Specification | Yes | We performed the experiment on a Mac with 2 2.4 GHz 6Core Intel Xeon processor and 32 GB of RAM. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as libraries or solvers, needed to replicate the experiments. |
| Experiment Setup | Yes | We utilize the method described in Section 3 to estimate linear measurements with a significance level of 0.05. |