Open-domain event extraction and embedding for Natural gas market prediction

Our paper is accepted to CEUR Workshop, 2020 (http://ceur-ws.org/Vol-2611/paper2.pdf)

Abstract

We propose an approach to predict the natural gas price in several days using historical price data and events from news headlines. Our event extraction method detects not only the occurrence of phenomenons but also the changes in attribution and characteristics. It also serves as one of the preliminaries for a knowledge graph for real-time events. Instead of using sentences embedding as a feature, we use every word of the extracted events, encode and organize them before feeding to the learning models. Empirical results show favorable results, in term of prediction performance, money saved and scalability.

Citation

@article{DBLP:journals/corr/abs-1912-11334,
  author    = {Minh Triet Chau and
               Diego Esteves and
               Jens Lehmann},
  title     = {Open-domain Event Extraction and Embedding for Natural Gas Market
               Prediction},
  journal   = {Proceedings of the 1st International Workshop on Cross-lingual Event-centric Open Analytics
co-located with the 17th Extended Semantic Web Conference (ESWC 2020)},
  volume    = {2611},
  year      = {2020},
  url       = {http://ceur-ws.org/Vol-2611/} 
}

Installation

We use virtualenv as the package management

1. Clone the repository
2. Install Python3
3. In the folder directory, run python3 -m venv venv to create a virtual environment
4. Run source venv/bin/activate
5. Run pip install -r requirements.txt
6. Install Spacy's English models
7. Download news headlines

Input

1. News: A CSV file with the following format, put it in input folder. The file used to read this is read_spot_market_v2 in util.py

date	price
02.07.2007	18.700
03.07.2007	19.510
04.07.2007	19.150
05.07.2007	21.700

2. Price: A CSV file with the following format. Place it in old_log folder

pub_date	info
2011-09-05T00:00Z	China Challenges U.S. Supremacy in Shale Gas
2011-09-07T00:00Z	Flow Starts in Gas Pipeline From Russia to Germany
2011-09-08T00:00Z	European Union Seeks Power to Block Bilateral Energy Deals
2011-09-14T00:00Z	Gas Flaring in North Dakota

Train

python3 train_event.py

Inference

python3 strategy_predict.py [--from_day from] [--to_day to]

Arguments

• --from_day from The starting day of the series (YYYY-MM-DD format)
• --to_day to (YYYY-MM-DD format) The ending day of the series (YYYY-MM-DD format)

Execute / mock trading

python3 strategy_excution_event.py [--from_day from] [--to_day to]

Arguments

• --from_day from The starting day of the series (YYYY-MM-DD format)
• --to_day to (YYYY-MM-DD format) The ending day of the series (YYYY-MM-DD format)

Generate Venn Graphs

Note that Reverb needs an period at the end of each headlines to extract relation from them

• To get sentences that have Verbs cat real_reverb_result.txt | cut -f2 | uniq > reverb_indicies.txt
• For our pipelines ./testPlain.bash models-MUN-SC-wn30 test.txt outputFile lib/dict/index.sense