This repository contains the code for the paper:
Superiority of Simplicity: A Lightweight Model for
Network Device Workload Prediction
Preprint version @ arxiv
First unpack the data/data.tar.gz archive.
The contained training_series_long.csv must be located in the
data directory.
Python 3.6 is required to run the script.
To run the script simply do:
python code/run.py
All 10000 series will be predicted. This might take a while
(~40 hours on one Nvidia Titan GPU, will run forever on CPU).
Alternatively it is possible to predict a subset of series.
python code/run.py --start 0 --end 10
This can be used for testing or for parallelization by running
this script several times and defining respective start and
end indices.
Example:
python code/run.py --start 0 --end 2500python code/run.py --start 2500 --end 5000python code/run.py --start 5000 --end 7500python code/run.py --start 7500 --end 10000
This will produce 4 submission files in data folder.