36 Stock Indices and Commodity Prices Time Series

Published: 19 Mar 2018 | Version 1 | DOI: 10.17632/x744mgjpkv.1
Contributor(s):

Description of this data

Time series in this dataset are used to create an interaction graph of markets and commodities to be used in machine learning prediction models. We used this dataset in our work and introduced a model named HyS3 and an algorithm named ConKruG.

Experiment data files

Steps to reproduce

The steps to reproduce are completely described in our paper. The RAW DATA come from yahoo finance, google finance, Federal Reserve Bank of St. Luis Economic Data, OPEC, U.S. Energy Administration and Tehran Stock Exchange official websites. The data are prepared in a way that all missing values are repeated by their previous data and all are in working days frequency without the Saturdays and Sundays. The preparation was done by pandas module in Python. More detailed information is in our paper. The papers information will be published after the acceptance.

peer reviewed

This data is associated with the following peer reviewed publication:

A hybrid supervised semi-supervised graph-based model to predict one-day ahead movement of global stock markets and commodity prices

Published in: Expert Systems with Applications

Latest version

  • Version 1

    2018-03-19

    Published: 2018-03-19

    DOI: 10.17632/x744mgjpkv.1

    Cite this dataset

    Negahdari Ki, Arash (2018), “36 Stock Indices and Commodity Prices Time Series”, Mendeley Data, v1 http://dx.doi.org/10.17632/x744mgjpkv.1

Categories

Finance, Econophysics, Machine Learning Algorithm, Semi-Supervised Learning, Supervised Learning

Mendeley Library

Organise your research assets using Mendeley Library. Add to Mendeley Library

Licence

CC BY 4.0 Learn more

The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

What does this mean?

This dataset is licensed under a Creative Commons Attribution 4.0 International licence. What does this mean? You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.