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 [1].
TACTiS: Transformer-Attentional Copulas for Time Series
Authors: Alexandre Drouin, รtienne Marcotte, Nicolas Chapados
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate these properties empirically and show that our model produces state-of-the-art predictions on multiple real-world datasets. 5. Experiments |
| Researcher Affiliation | Industry | Alexandre Drouin * 1 Etienne Marcotte * 1 Nicolas Chapados * 1 1Service Now Research. Correspondence to: All authors <EMAIL>. |
| Pseudocode | No | The paper describes the model architecture and training procedure in text and with diagrams (e.g., Figure 1, Figure 2) but does not include a formal pseudocode or algorithm block. |
| Open Source Code | Yes | Code available at https://github.com/servicenow/tactis. |
| Open Datasets | Yes | We consider five real-world datasets from the Monash Time Series Forecasting Repository (Godahewa et al., 2021): electricity, fred-md, kdd-cup, solar-10min, and traffic (see C.1 for details). These were selected for being high-dimensional (107 862 variables), exempt of missing values, and sampled at diverse frequencies (10 min., hourly, monthly). Each dataset was downloaded from the Monash Repository using Gluon TS (Alexandrov et al., 2020) (links to the exact versions are provided in Tab. 3). |
| Dataset Splits | Yes | From the remaining time steps, a window at the end of length equals to 7 times the prediction length is reserved to serve as the validation set, which is used to compute the metrics used to select the best hyperparameters. The hyperparameter search described in C.3 is done using only data before ฯ1, while the i-th model is trained using only the data before ฯi. |
| Hardware Specification | Yes | Each training is done in a Docker container giving access to an NVIDIA Tesla P100 GPU with 12 GB of GPU RAM, 2 CPU cores, and 32 GB of CPU RAM. |
| Software Dependencies | No | The paper mentions software like PyTorch (Paszke et al., 2019), Py Torch TS (Rasul, 2021a), and Gluon TS (Alexandrov et al., 2020) but does not provide specific version numbers for these libraries, only citations to their respective papers. |
| Experiment Setup | Yes | In this section, we list the values considered for each hyperparameter of each method. The ranges considered for the baselines are inspired by those published in their respective papers and implementations. Tab. 4 shows the hyperparameters for the TACTi S-TT model. |