PromptCast: A New Forecasting Paradigm

Introduction

This repository is the reporisity 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.

Numerical Time Series Forecasting vs. PromptCast

Exisiting numerical-based forecasting VS. Prompt-based forecasting

PromptCast Evaluation Metrics

• RMSE
• MAE
• Missing Rate: whether the numerical forecasting target can be decoded (via string parsing) from the generated output prompts.

PISA Dataset

Forecasting Scenarios

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   

Benchmark Results

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} 
}