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..
Learning Deep Time-index Models for Time Series Forecasting
Authors: Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real world datasets in the long sequence time-series forecasting setting demonstrate that our approach achieves competitive results with state-of-the-art methods, and is highly efficient. Code is available at https: //github.com/salesforce/Deep Time. We conduct extensive experiments on the long sequence time series forecasting (LSTF) setting, demonstrating Deep Time to be extremely competitive with state-of-the-art baselines. At the same time, Deep Time is highly efficient in terms of runtime and memory. |
| Researcher Affiliation | Collaboration | 1Salesforce Research Asia 2School of Computing and Information Systems, Singapore Management University. Correspondence to: Gerald Woo <EMAIL>, Chenghao Liu <EMAIL>. |
| Pseudocode | Yes | Pseudocode of Deep Time is available in Appendix A. Algorithm 1 Py Torch-Style Pseudocode of Closed-Form Ridge Regressor. Algorithm 2 Py Torch-Style Pseudocode of Deep TIMe. |
| Open Source Code | Yes | Code is available at https: //github.com/salesforce/Deep Time. |
| Open Datasets | Yes | Experiments are performed on 6 real-world datasets Electricity Transformer Temperature (ETT), Electricity Consuming Load (ECL), Exchange, Traffic, Weather, and Influenzalike Illness (ILI) with full details in Appendix F. Appendix F states for ETT: 1https://github.com/zhouhaoyi/ETDataset. |
| Dataset Splits | Yes | The datasets are split into train, validation, and test sets chronologically, following a 70/10/20 split for all datasets except for ETTm2 which follows a 60/20/20 split, as per convention. |
| Hardware Specification | Yes | All experiments are performed on an Nvidia A100 GPU. |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | Further implementation details on Deep Time are reported in Appendix G, and detailed hyperparameters are reported in Appendix H. Table 6 in Appendix H lists: Epochs 50, Learning rate 1e-3, Warm up epochs 5, Batch size 256, Early stopping patience 7, Max gradient norm 10.0, Layers 5, Layer size 256, λ initialization 0.0, Scales [0.01, 0.1, 1, 5, 10, 20, 50, 100], Fourier features size 4096, Dropout 0.1. |