Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction
Authors: Hsiang-Fu Yu, Nikhil Rao, Inderjit S. Dhillon
NeurIPS 2016 | Venue PDF | 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 EMAIL Nikhil Rao Technicolor Research EMAIL Inderjit S. Dhillon University of Texas at Austin EMAIL |
| 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]. |