| Field | Value |
|---|---|
| Title | DLM |
| Type | Source Code |
| Language | Python |
| License | |
| Status | Research Code |
| Update Frequency | NO |
| Date Published | 2019-01-31 |
| Date Updated | 2019-01-31 |
| Portal | https://github.com/tulip-lab/open-code |
| URL | https://github.com/tulip-lab/open-code/tree/master/DLM |
| Publisher | TULIP Lab |
| Point of Contact | A/Prof. Gang Li |
This package (DLM) is the deep learning algorithm for tourism demand forecasting. Please be aware that:
- The training of DLM needs extra efforts based on specific data set, and direct running of the provided code DOES NOT always generate the promised performance.
- For the training of the model on the data set, please spend your own patient time and the code publisher will NOT provide assistance on this issue.
If you use it for a scientific publication, please include a reference to this paper.
- Rob Law, Gang Li, Davis Fong, Xin Han (2019). Tourism Demand Forecasting: A Deep Learning Approach. Annals of Tourism Research, Vol 75, March 2019, Page 410-423
BibTex information:
@article{LLFHDeep2019,
title = {Tourism Demand Forecasting: A Deep Learning Approach},
volume = {75},
doi = {10.1016/j.annals.2019.01.014},
journal = {Annals of Tourism Research},
author = {Law, Rob and Li, Gang and Fong, Davis Ka Chio and Han, Xin},
month = March,
year = {2019},
keywords = {Big data analytics, Deep Learning, Search query data,Tourism Demand Forecast},
pages = {410-423},
}
The related dataset for above paper can be found at TULIP Lab Open-Data:
Macau2018: Tourism Demand Forcasting Data for Macau from January 2011 to August 2018
- Python 3.6
- Keras
- Tensorflow
- Window-based input (window size is 12)
edit Setting.py % set paramaters
python Preprocess.py % data preprocess
python Eval.py % model evaluation