Learning Deep Time-index Models for Time Series Forecasting
Authors: Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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 <gwoo@salesforce.com>, Chenghao Liu <chenghao.liu@salesforce.com>. |
| 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. |