This project is an implementation of the paper Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks .
This project uses python 3.6 and the PIP the packages included in requirements.txt
virtualenv -p python3 venv
source /venv/bin/activate
pip install -r requirements.txt
Create folder /data/ and store raw text format dataset in it. The exchange dataset is already present in the data folder.
For other datasets it is advisable to run on GPU or colab version
The raw dataset is in https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014. It is the electricity consumption in kWh was recorded every 15 minutes from 2011 to 2014. Because the some dimensions are equal to 0. So we eliminate the records in 2011. Final we get data contains electircity consumption of 321 clients from 2012 to 2014. And we converted the data to reflect hourly consumption.
The raw data is in http://pems.dot.ca.gov. The data in this repo is a collection of 48 months (2015-2016) hourly data from the California Department of Transportation. The data describes the road occupancy rates (between 0 and 1) measured by different sensors on San Francisco Bay area freeways.
The raw data is in http://www.nrel.gov/grid/solar-power-data.html : It contains the solar power production records in the year of 2006, which is sampled every 10 minutes from 137 PV plants in Alabama State.
Run the LSTNet.ipynb file on jupyter notebook.
The code needs a gpu environment for fast execution for datasets of elecrticity, solar and traffic.
Upload the file MultivariateTimeSeriesForecasting(colab_version).ipynb on a new cuda10 enabled colab project. Add this link to your drive for retrieving the datasets.