https://github.com/huawei-noah/BHT-ARIMA

Code for paper: Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting (AAAI-20)
https://github.com/huawei-noah/BHT-ARIMA

arima-forecasting tensor-decomposition tensor-factorization time-series

Last synced: about 2 months ago
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Code for paper: Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting (AAAI-20)

• Host: GitHub
• URL: https://github.com/huawei-noah/BHT-ARIMA
• Owner: huawei-noah
• License: mit
• Created: 2020-02-25T02:43:54.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2021-06-08T01:29:04.000Z (over 3 years ago)
• Last Synced: 2024-05-21T11:50:05.840Z (4 months ago)
• Topics: arima-forecasting, tensor-decomposition, tensor-factorization, time-series
• Language: Python
• Size: 61.5 KB
• Stars: 97
• Watchers: 4
• Forks: 37
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-time-series - Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting

README

        
# BHT-ARIMA

A tensor decomposition-based time series forecasting algorithm, which tactically incorporates the unique advantages of Hankelization, low-rank Tucker decomposition and ARIMA into a unified framework.  

More details (including parameter settings) refer to [the original paper](https://arxiv.org/abs/2002.12135).

### Paper

- **"[Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting](https://arxiv.org/abs/2002.12135)", AAAI-20**

### Datasets

  

Traffic dataset. The traffic data is originally collected from California department of transportation 1 and describes the road occupy rate of Los Angeles County highway network.We here use the same subset used in (Yu, Yin, and Zhu 2017) which selects **228 sensors** randomly. And We take **the first 40 time points** of them as data of our demo

### Getting Started

#### Prerequisites  

- python 3.5+

- python libraries

  - tensorly

  - scipy

  - numpy

  - pandas 

#### Run

```python

python main.py

```

### License

© Contributors, 2019. Licensed under an [MIT](LICENSE) license.