Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future
Authors: Harshavardhan Kamarthi, Alexander Rodríguez, B. Aditya Prakash
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments demonstrate that our method refines the performance of diverse set of top models for COVID-19 forecasting and GDP growth forecasting. |
| Researcher Affiliation | Academia | Harshavardhan Kamarthi, Alexander Rodríguez, B. Aditya Prakash College of Computing Georgia Institute of Technology {harsha.pk,arodriguezc,badityap}@gatech.edu |
| Pseudocode | No | The paper describes the model components and their interactions but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | We also release the code and datasets at www.github.com/Aditya Lab/Back2Future. |
| Open Datasets | Yes | We collected and pre-processed important publicly available signals from a variety of trusted sources that are relevant to COVID-19 forecasting to form the COVID-19 Surveillance Dataset (Co VDS)... The code for B2F and the Co VDS dataset is publicly available at https://github.com/Aditya Lab/Back2Future. |
| Dataset Splits | Yes | We tuned the model hyperparameters using data from June 2020 to Aug. 2020 and tested it on the rest of dataset including completely unseen data from Jan. 2021 to June 2021. |
| Hardware Specification | Yes | All experiments were run in an Intel i7 4.8 GHz CPU with Nvidia Tesla A4 GPU. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for libraries or frameworks used in the experiments. |
| Experiment Setup | Yes | We tuned the model hyperparameters using data from June 2020 to Aug. 2020... We observed that setting hyperparameter τ = c|F| where c {2, 3, 4, 5} provided best results... We also found setting l = 5 provided the best performance. |