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A Toolkit for Multivariate Time Series Data

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MVTS Data Toolkit v0.2.6

A Toolkit for Pre-processing Multivariate Time Series Data

  • Title: MVTS Data Toolkit: A Toolkit for Pre-processing Multivariate Time Series Data
  • Journal: SoftwareX Journal > (Elsevier) -- [under-review]
  • Authors: Azim Ahmadzadeh >, Kankana Sinha >, Berkay Aydin >, Rafal A. Angryk >
  • Demo Author: Azim Ahmadzadeh
  • Last Modified: May 03, 2020

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Abstract: We developed a domain-independent Python package to facilitate the preprocessing routines required in preparation of any multi-class, multivariate time series data. It provides a comprehensive set of 48 statistical features for extracting the important characteristics of time series. The feature extraction process is automated in a sequential and parallel fashion, and is supplemented with an extensive summary report about the data. Using other modules, different data normalization methods and imputations are at users' disposal. To cater the class-imbalance issue, that is often intrinsic to real-world datasets, a set of generic but user-friendly, sampling methods are also developed.

This package provides:

  • Feature Collection: A collection of 48 statistical features for analysis of time series,
  • Feature Extraction: An automated feature-extraction process, with both parallel and sequential execution capabilities,
  • Visualization: Several quick and easy visualization methods for analysis of the extracted features,
  • Data Analysis: A quick analysis of the mvts data and the extracted features,
  • Normalization: A set of data transformation tools for normalization of the extracted features,
  • Sampling: A set of generic methods to provide an array of undersampling and oversampling remedies for balancing the class-imbalance datasets.

PyPI license PyPI license PyPI license


Try it online

Click on the badge below to try the demo provided in the notebook demo.ipynb, online:


Install it from PyPI

You can install this package, directly from Python Package Index (PyPI), using pip as follows:

  • Linux/Mac OS:

    pip install mvtsdatatoolkit

  • Windows:

Note: On windows, the Microsooft Visual C++ must be updated. Otherwise the error Microsoft Visual C++ 14.0 is required might terminate the installation. To solve this issue, see this Medium post that elaborates on this short Stackoverflow answer.

PyPI license

See Documentation

Check out the documentation of the project here:

PyPI license

Data Rules:

MVTS Files

It is assumed that the input dataset is a collection of multivariate time series (mvts), following these assumptions:

  1. Each mvts is stored in a tab-delimited, csv file. Each column represents either the time series or some metadata such as timestamp. An mvts dataset with t time series and k metadata columns, each of length d, has a dimension of d * (t + k).

  2. File names can also be used to have some metadata encoded using a tag followed by [], for each piece of info. The tag can be any string of characters and indicates what that piece of info is about, and the actual information should be stored inside the proceeding square brackets. For example, the file-name A_id[123]_lab[1].csv indicates that this mvts is assigned the id 123 and the label 1. If tags are used, during the feature extraction process, the metadata will be extracted and also added to the tabular extracted features automatically. To learn more about how the tags can be used see the documentation in .

  3. If the embedded values contain paired braces within [], (e.g. for id, id[123[001]]), then the metadata extractor would still be able to extract the info correctly, however for unpaired braces (e.g. for id, id[123[001]) it will raise an exception.

Main Components:


A Jupyer notebook is provided to give a tour of the main functionalities of the package. Running the demo is fairly simple. You need the notebook and the example input.

1. Notebook

The Jupyer notebook demo is at the root directory.

Users can try the demo in one of the three ways listed below:

  • Online: click on the binder badge (see above) and you will be able to follow the demo on a remote server online. This is the simplest way to try the demo. A user would only need access to the Internet for this method.
  • Locally with package: pip install the mvtsdatatoolkit package on your local machine and download and run the nodebook from the same (virtual or physical) machine. (See the next section for more details.)
  • Locally with source: Clone the mvtsdata_toolkit project, install the dependencies (listed in requirements.txt and run the notebook from the same (virtual or physical) machine.

2. Input

A dataset of 2000 mvts files and a configuration file specifically defined for this dataset will be downloaded along the steps of this demo.

The provided dataset is a subset of the benchmark dataset called Space Weather ANalytics for Solar Flares (SWAN-SF) [2] .

Need Help Running Demo Locally?

Follow the steps below to run the demo notebook on your local machine using virtualenv and without having to clone the project. If you are more comfortable with conda/anaconda make appropriate adjustments.

(Commands below are specific to Unix-base systems)

  • Create a new directory and cd into it:
mkdir mvts_demo
cd mvts_demo/
  • Inside mvts_demo directory create a new virtualenv called venv and activate the virtual environment:
virtualenv -p /usr/bin/python3.6 venv
source venv/bin/activate
  • Install mvtsdatatoolkit (this will consequently install notebook library among other required libraries):
pip install mvtsdatatoolkit
  • Download the notebook and start the Jupyter notebook:
jupyter notebook

Example Code Snippets

In following examples, the string '/PATH/TO/CONFIG.YML' points to the user's configuration file.

Data Analysis

This package allows analysis of both raw mvts data and the extracted features.

Using mvts_data_analysis module users can easily get a glimpse of their raw data.

from mvtsdatatoolkit.data_analysis import MVTSDataAnalysis
mda = MVTSDataAnalysis('/PATH/TO/CONFIG.YML')
                    params_name=['TOTUSJH', 'TOTBSQ', 'TOTPOT'])

Then, mda.print_stat_of_directory() gives the size of the data, in total and on average, and mda.summary returns a dataframe with several statistics on each of the time series. The statistics are Val-Count, Null-Count, mean, min, max, and the quartiles 25th, 50th (= median), 75th.

For large datasets, it is recommended to use the parallel version of this method, as follows:

                                params_name=['TOTUSJH', 'TOTBSQ', 'TOTPOT'],)

which utilizes 4 processes to extract the summary statistics in parallel, on the first 50 mvts files. For more details about the parallel computation see the paper [1].

Using extracted_features_analysis module users can also get some analyses from the extracted features (see Section Feature Extraction). Suppose the dataframe of the extracted features is loaded as a pandas dataframe into a variable called extracted_features_df. Then,

from mvtsdatatoolkit.data_analysis import ExtractedFeaturesAnalysis
efa = ExtractedFeaturesAnalysis(extracted_features_df, excluded_col=['id'])

that excludes the column id of the extracted features from the analysis and computes a set of summary statistics on all extracted features.

After the summary is computed, the following methods can be used:


Feature Extraction

This snippet shows how feature_extractor module can be used, for extracting 4 statistics (i.e., min, max, median, and mean), from 3 time series parameteres (i.e., TOTUSJH, TOTBSQ, and TOTPOT) available in the provided dataset.

from mvtsdatatoolkit.features import FeatureExtractor

fe = FeatureExtractor(path_to_config='/PATH/TO/CONFIG.YML')
fe.do_extraction(features_name=['get_min', 'get_max', 'get_median', 'get_mean'],
                 params_name=['TOTUSJH', 'TOTBSQ', 'TOTPOT'], first_k=50)

Note that user's configuration file must contain the path to the raw mvts using the key PATH_TO_MVTS.

To benefit from the parallel execution, do:

                             features_index=[0, 1, 2, 3],
                             params_index=[0, 1, 2], first_k=50)

Here, for the sake of providing a richer example, we used features_index and params_index instead of their names (that was already shown in the previous example). This numeric mapping of features and parameters makes it easier to deal with a long array of lengthy names. These two lists will be mapped to the list of parameters and features provided in the user's configuration file, under the keys MVTS_PARAMETERS and STATISTICAL_FEATURES, respectively.

In FeatureExtractor class, several plotting functionalities are implemented that can be easily used as follows:

params = ['TOTUSJH_median', 'TOTUSJH_mean', 'TOTBSQ_median', 'TOTBSQ_mean']


After the statistical features are extracted from the mvts data, to remedy the class-imbalance issue (if exists) a set of generic sampling methods are provided in sampler module.

from mvtsdatatoolkit.sampling.sampler import Sampler

sampler = Sampler(extracted_features_df, label_col_name='lab')
sampler.sample(desired_populations={'N': 100, 'Y': 100})

Assumming that the dataset has the class labels Y and N, this snippet of code randomly samples 100 instances of the N class and 100 instances of the Y class instances. If either of the classes does not have enough samples, then after the entire samples are taken, the remaining needed instances will be sampled randomly with replacement. Depending on the provided populations, this method could turn into an undersampling or oversampling function.

Users can use ratio instead of size as follows:

sampler.sample(desrired_ratios = {'N': 0.50, 'Y': -1})

which means take 50% of the entire population would be sampled from N-class instances, and all of Y-class instances will also be passed to the sampled data.

For other approaches, see the /demo.


The extracted features often require normalization. Using the module normalizer , the features can be quickly normalized as follows:

from mvtsdatatoolkit.normalizing import normalizer
df_normalized = normalizer.zero_one_normalize(extracted_features_df)

Again, extracted_features_df is assumed to be a pandas dataframe of the extracted features.

In this module, the following four normalizers are implemented on top of the scikit-learn library.

  • zero_one_normalizer()
  • negativeone_one_normalize()
  • standardize()
  • robust_standardize()

Extra files:

  • bitbucket-pipelines.yml is a configuration file for pipelining the deployment steps before each release.
  • keeps track of some constant variables such as root path.
  • demo.ipynb is the demo Jupyter notebook that can walk the interested users through the functionalities this toolkit provides.
  • has the content of this very manual.
  • requirements.txt keeps track of all dependencies.
  • is used to generate the binary files needed for generating the pip-installble version of this package.


Currently, this package is under review in SoftwareX journal. If you are interested in using this, I can share the manuscript with you. Till it is published, it can be cited as follows:

  title={MVTS-Data Toolkit: A Python Package for Preprocessing Multivariate Time Series Data}},
  author={Azim Ahmadzadeh, Kankana Sinha, Berkay Aydin, Rafal A. Angryk},


[1] A. Ahmadzadeh, K. Sinha, 2020. "MVTS-Data Toolkit: A Python Package for Preprocessing Multivariate Time Series Data", (under review 2020))

[2] Angryk, R.A., Martens, P.C., Aydin, B., Kempton, D., Mahajan, S.S., Basodi, S., Ahmadzadeh, A., Cai, X., Boubrahimi, S.F., Hamdi, S.M., Schuh, M.A. and Georgoulis, M.K., 2019. "Multivariate Time Series Dataset for Space Weather Data Analytics". Sci. Data, Nature, submitted (2019).

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