Metadata-Version: 2.1
Name: tspymfe
Version: 0.0.2
Summary: Univariate time-series expansion for Pymfe package
Home-page: https://github.com/FelSiq/ts-pymfe
Author: Felipe Siqueira
Author-email: felipe.siqueira@usp.br
License: UNKNOWN
Description: # ts-pymfe
        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.
        
        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
        
        ## Install
        From pip:
        ``
        pip install -U tspymfe
        ``
        or:
        ``
        python3 -m pip install -U tspymfe
        ``
        
        ## Usage
        To extract the meta-features, the API behaves pretty much like the original Pymfe API:
        ```python
        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)
        ```
        
        ## Dev-install
        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](https://github.com/FelSiq/ts-pymfe/tree/master/data) 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
        ```
        
        # Main references
        ## Papers
        1. [T.S. Talagala, R.J. Hyndman and G. Athanasopoulos. Meta-learning how to forecast time series (2018).](https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf).
        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).](https://www.monash.edu/business/ebs/research/publications/ebs/wp10-16.pdf)
        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)](https://www.sciencedirect.com/science/article/abs/pii/S0925231210001074)
        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).][1]
        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).](https://royalsocietypublishing.org/doi/full/10.1098/rsif.2013.0048)
        
        
        ## Books
        1. [Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2. Accessed on April 29 2020.](https://otexts.com/fpp2/)
        
        
        ## Packages
        1. [tsfeatures (R language)](https://github.com/robjhyndman/tsfeatures)
        2. [hctsa (Matlab language)](https://github.com/benfulcher/hctsa)
        
        [1]: https://www.cell.com/cell-systems/fulltext/S2405-4712(17)30438-6
        
        ## Data
        Data sampled from: https://comp-engine.org/
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
