This is the repository of PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting (TKDE2023). PISA is a large-scale dataset including three real-world forecasting scenarios (three sub-sets) with 311,932 data instances in total. It is designed to support and facilitate the novel PromptCast task proposed in the paper.
Exisiting numerical-based forecasting VS. Prompt-based forecasting
- RMSE
- MAE
- Missing Rate: whether the numerical forecasting target can be decoded (via string parsing) from the generated output prompts.
The proposed PISA dataset contrains three real-world forecasting scenarios:
- CT: city temperature forecasting
- ECL: electricity consumption forecasting
- SG: humana mobility visitor flow forecasting
Details of three sub-sets
Folder Structure (see Dataset)
Dataset |── PISA-Prompt │── CT │-- train_x_prompt.txt │-- train_y_prompt.txt │-- val_x_prompt.txt │-- val_y_prompt.txt │-- test_x_prompt.txt │-- test_y_prompt.txt │── ECL │-- train_x_prompt.txt │-- train_y_prompt.txt │-- val_x_prompt.txt │-- val_y_prompt.txt │-- test_x_prompt.txt │-- test_y_prompt.txt │── SG │-- train_x_prompt.txt │-- train_y_prompt.txt │-- val_x_prompt.txt │-- val_y_prompt.txt │-- test_x_prompt.txt │-- test_y_prompt.txt Please check Benchmark folder for the implementations of benchmarked methods.
RMSE and MAE performance
Missing Rate results
Results under train-from-scratch and cross-scenario zero-shot settings
If you think this repo is useful, please cite our papers
@ARTICLE{xue2023promptcast, author={Xue, Hao and Salim, Flora D.}, journal={IEEE Transactions on Knowledge and Data Engineering}, title={PromptCast: A New Prompt-Based Learning Paradigm for Time Series Forecasting}, year={2023}, volume={}, number={}, pages={1-14}, doi={10.1109/TKDE.2023.3342137}} @inproceedings{xue2022translating, title={Translating human mobility forecasting through natural language generation}, author={Xue, Hao and Salim, Flora D and Ren, Yongli and Clarke, Charles LA}, booktitle={Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining}, pages={1224--1233}, year={2022} } @inproceedings{xue2022leveraging, title={Leveraging language foundation models for human mobility forecasting}, author={Xue, Hao and Voutharoja, Bhanu Prakash and Salim, Flora D}, booktitle={Proceedings of the 30th International Conference on Advances in Geographic Information Systems}, pages={1--9}, year={2022} }