A fast GPU implementation of STL decomposition with missing values
Project description
HaSTL [ˈheɪstiɛl]: A fast GPU implementation of batched Seasonal and Trend decomposition using Loess (STL) [1] with missing values and support for both CUDA and OpenCL (a rather slow C backend is also available). Loosely based on stlplus, a popular library for the R programming language. The GPU code is written in Futhark, a small functional language that compiles to efficient parallel code.
Requirements
You would need a working OpenCL or CUDA installation, C compiler and these Python packages:
futhark-ffi>=0.13.0
wheel
Installation
You may want to run the program in a Python virtual environment. Create it via:
python -m venv env
Then, activate the virtual environment via:
. env/bin/activate
Upgrade pip via:
pip install --upgrade pip
To install the package from the sources, first get the current stable release via:
git clone https://github.com/mortvest/hastl
Install the bfast dependencies via:
pip install -r requirements.txt
Afterwards, you can compile all the backends and install the package (this will take a while):
python setup.py sdist bdist_wheel pip install .
Usage
Set backend to “cuda”, “opencl” or “c” and run:
from hastl import STL stl = STL(backend=backend) seasonal, trend, remainder = stl.fit(data, n_p=12)
References
[1] Cleveland, Robert B., et al. “STL: A seasonal-trend decomposition.” J. Off. Stat 6.1 (1990): 3-73.
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