diff --git a/README.md b/README.md index dd0eea0..8656169 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,16 @@ -# DeepTIMe: Deep Time-Index Meta-Learning for Non-Stationary Time-Series Forecasting +# DeepTime: Deep Time-Index Meta-Learning for Non-Stationary Time-Series Forecasting



-Figure 1. Overall approach of DeepTIMe. +Figure 1. Overall approach of DeepTime.

-Official PyTorch code repository for the [DeepTIMe paper](https://arxiv.org/abs/2207.06046). +Official PyTorch code repository for the [DeepTime paper](https://arxiv.org/abs/2207.06046). -* DeepTIMe is a deep time-index based model trained via a meta-learning formulation, yielding a strong method for +* DeepTime is a deep time-index based model trained via a meta-learning formulation, yielding a strong method for non-stationary time-series forecasting. -* Experiments on real world datases in the long sequence time-series forecasting setting demonstrates that DeepTIMe +* Experiments on real world datases in the long sequence time-series forecasting setting demonstrates that DeepTime achieves competitive results with state-of-the-art methods and is highly efficient. ## Requirements @@ -55,7 +55,7 @@ the `storage/experiments/experiment_name/metrics.npy` file. ## Main Results -We conduct extensive experiments on both synthetic and real world datasets, showing that DeepTIMe has extremely +We conduct extensive experiments on both synthetic and real world datasets, showing that DeepTime has extremely competitive performance, achieving state-of-the-art results on 20 out of 24 settings for the multivariate forecasting benchmark based on MSE.

@@ -94,7 +94,7 @@ a `.gin` configuration file based on the `build.variables_dict` argument. ## Acknowledgements -The implementation of DeepTIMe relies on resources from the following codebases and repositories, we thank the original +The implementation of DeepTime relies on resources from the following codebases and repositories, we thank the original authors for open-sourcing their work. * https://github.com/ElementAI/N-BEATS @@ -105,7 +105,7 @@ authors for open-sourcing their work. Please consider citing if you find this code useful to your research. 

@article{woo2022deeptime,
-    title={DeepTIMe: Deep Time-Index Meta-Learning for Non-Stationary Time-Series Forecasting},
+    title={DeepTime: Deep Time-Index Meta-Learning for Non-Stationary Time-Series Forecasting},
     author={Gerald Woo and Chenghao Liu and Doyen Sahoo and Akshat Kumar and Steven C. H. Hoi},
     year={2022},
     url={https://arxiv.org/abs/2207.06046},