A backup for the pymfe expansion for time-series data. Currently, this repository contains the methods for meta-feature extraction and an modified pymfe core to run extract the meta-features.

Please note that tspymfe is not intended to be a stand-alone package, and will be oficially merged (hopefully soon) to the original Pymfe package. Until then, this package is available as a beta version.

There is 149 distinct metafeature extraction methods in this version, distributed in the following groups:

1. General
2. Local statistics
3. Global statistics
4. Statistical tests
5. Autocorrelation
6. Frequency domain
7. Information theory
8. Randomize
9. Landmarking
10. Model based

From pip:

pip install -U tspymfe

or:

python3 -m pip install -U tspymfe

To extract the meta-features, the API behaves pretty much like the original Pymfe API:

import pymfe.tsmfe
import numpy as np

# random time-series
ts = 0.3 * np.arange(100) + np.random.randn(100)

extractor = pymfe.tsmfe.TSMFE()
extractor.fit(ts)
res = extractor.extract()

print(res)

If you downloaded directly from github, install the required packages using:

pip install -Ur requirements.txt

You can run some test scripts:

python test_a.py <data_id> <random_seed> <precomp 0/1>
python test_b.py <data_id> <random_seed> <precomp 0/1>

Where the first argument is the test time-series id (check data/comp-engine-export-sample.20200503.csv file.) and must be between 0 (inclusive) and 19 (also inclusive), the random seed must be an integer, and precomp is a boolean argument ('0' or '1') to activate the precomputation methods, used to calculate common values between various methods and, therefore, speed the main computations.

Example:

python test_a.py 0 16 1
python test_b.py 0 16 1

The code format style is checked using flake8, pylint and mypy. You can use the Makefile to run all verifications by yourself:

pip install -Ur requirements-dev.txt
make code-check

1. T.S. Talagala, R.J. Hyndman and G. Athanasopoulos. Meta-learning how to forecast time series (2018)..
2. Kang, Yanfei., Hyndman, R.J., & Smith-Miles, Kate. (2016). Visualising Forecasting Algorithm Performance using Time Series Instance Spaces (Department of Econometrics and Business Statistics Working Paper Series 10/16).
3. C. Lemke, and B. Gabrys. Meta-learning for time series forecasting and forecast combination (Neurocomputing Volume 73, Issues 10–12, June 2010, Pages 2006-2016)
4. B.D. Fulcher and N.S. Jones. hctsa: A computational framework for automated time-series phenotyping using massive feature extraction. Cell Systems 5, 527 (2017).
5. B.D. Fulcher, M.A. Little, N.S. Jones. Highly comparative time-series analysis: the empirical structure of time series and their methods. J. Roy. Soc. Interface 10, 83 (2013).

1. Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2. Accessed on April 29 2020.

1. tsfeatures (R language)
2. hctsa (Matlab language)

Data sampled from: https://comp-engine.org/