Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction
Authors: Hsiang-Fu Yu, Nikhil Rao, Inderjit S. Dhillon
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show the superiority of TRMF in terms of scalability and prediction quality. In particular, TRMF is two orders of magnitude faster than other methods on a problem of dimension 50,000, and generates better forecasts on real-world datasets such as Wal-mart E-commerce datasets. We demonstrate the superiority of the proposed approach via extensive experimental results in Section 5 |
| Researcher Affiliation | Collaboration | Hsiang-Fu Yu University of Texas at Austin rofuyu@cs.utexas.edu Nikhil Rao Technicolor Research nikhilrao86@gmail.com Inderjit S. Dhillon University of Texas at Austin inderjit@cs.utexas.edu |
| Pseudocode | No | The paper describes the mathematical formulations and optimization steps for TRMF, including updates for F, X, and W, but it does not provide any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code for the methodology, nor does it include links to a code repository. |
| Open Datasets | Yes | The data sets electricity and traffic are obtained from the UCI repository, while walmart-1 and walmart-2 are two propriety datasets from Walmart E-commerce containing weekly sale information. |
| Dataset Splits | Yes | For each method and data set, we perform a grid search over various parameters (such as k, λ values) following a rolling validation approach described in [11]. |
| Hardware Specification | No | The paper discusses computational scalability and efficiency (e.g., 'two orders of magnitude faster', 'O(|L|Tk2) time'), but it does not specify any particular hardware used for running the experiments (e.g., CPU type, GPU model, memory). |
| Software Dependencies | No | The paper mentions software like the 'R-DLM package [12]' but does not provide specific version numbers for any software dependencies or libraries used in their implementation (e.g., 'Python 3.x', 'PyTorch 1.x'). |
| Experiment Setup | Yes | For L, we use {1, 2, . . . , 8} for synthetic, {1, . . . , 24}[{7 24, . . . , 8 24 1} for electricity and traffic, and {1, . . . , 10}[ {50, . . . , 56} for walmart-1 and walmart-2 to capture seasonality. For each method and data set, we perform a grid search over various parameters (such as k, λ values) following a rolling validation approach described in [11]. |