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..
Deep Factors for Forecasting
Authors: Yuyang Wang, Alex Smola, Danielle Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments with synthetic and real-world data to provide evidence for the practical effectiveness of our approach. |
| Researcher Affiliation | Industry | Yuyang Wang 1 Alex Smola 1 Danielle C. Maddix 1 Jan Gasthaus 1 Dean Foster 1 Tim Januschowski 1 1Amazon Research. |
| Pseudocode | Yes | Algorithm 1 Training Procedure for Deep Factor Models with Random Effects. |
| Open Source Code | No | The paper states that algorithms are implemented in MXNet Gluon but does not provide a link or explicit statement that the source code for their specific method is publicly available. |
| Open Datasets | Yes | We conduct experiments with synthetic and real-world data to provide evidence for the practical effectiveness of our approach... electricity (E) and traffic (T) from the UCI data set (Dheeru & Karra Taniskidou, 2017; Yu et al., 2016), nyc taxi (N) (Taxi & Commission, 2015) and uber (U) (Flowers, 2015) (cf. Appendix B.2). |
| Dataset Splits | No | The paper specifies the training length and mentions using a "test set" but does not explicitly detail a separate validation set split or how data was partitioned into training, validation, and test sets with percentages or counts. |
| Hardware Specification | Yes | We use a p3.8xlarge Sage Maker instance in all our experiments. |
| Software Dependencies | No | The paper states: "Our algorithms are implemented in MXNet Gluon (Chen et al., 2015)". While MXNet Gluon is named, a specific version number is not provided, nor are other software dependencies with their versions. |
| Experiment Setup | Yes | The Deep Factor model has 10 global factors with a LSTM cell of 1-layer and 50 hidden units. The noise LSTM has 1-layer and 5 hidden units. For a fair comparison with Deep AR, we use a comparable number of model parameters, that is, an embedding size of 10 with 1-layer and 50 hidden LSTM units. The student-t likelihood in Deep AR is chosen for its robust performance. The same model structure is chosen for MQ-RNN, and the decoder MLP has a single hidden layer of 20 units. We use the adam optimization method with the default parameters in Gluon to train the DF-RNN and MQ-RNN. We use the default training parameters for Deep AR. |