{
    "info": {
        "author": "Samuel Monnier",
        "author_email": "samuel.monnier@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering"
        ],
        "description": "timeseriescv\n------------\n\nThis package implements two cross-validation algorithms suitable to evaluate machine learning models based on time series\ndatasets where each sample is tagged with a prediction time and an evaluation time.\n\nRessources\n~~~~~~~~~~\n\n* `A Medium post <https://medium.com/@samuel.monnier/cross-validation-tools-for-time-series-ffa1a5a09bf9>`_  providing some motivation and explaining the cross-validation algorithms implemented here in more detail.\n\n* `Advances in financial machine learning <https://www.wiley.com/en-us/Advances+in+Financial+Machine+Learning-p-9781119482086>`_ by Marcos Lopez de Prado. An excellent book that inspired this package.\n\n* `Github repository <https://github.com/sam31415/timeseriescv/>`_\n\n\nInstallation\n~~~~~~~~~~~~\n\ntimeseriescv can be installed using pip:\n\n    >>> pip install timeseriescv\n\nContent\n~~~~~~~\n\nFor now the package contains two main classes handling cross-validation:\n\n* ``PurgedWalkForwardCV``: Walk-forward cross-validation with purging.\n* ``CombPurgedKFoldCV``: Combinatorial cross-validation with purging and embargoing.\n\nRemarks concerning the API\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe API is as similar to the scikit-learn API as possible. Like the scikit-learn cross-validation classes, the ``split``\nmethod is a generator that yields a pair of numpy arrays containing the positional indices of the samples in the train\nand validation set, respectively. The main differences with the scikit-learn API are:\n\n* The ``split`` method takes as arguments not only the predictor values ``X``, but also the prediction times ``pred_times`` and the evaluation times ``eval_times`` of each sample.\n* To stay as close to the scikit-learn API as possible, this data is passed as separate parameters. But in order to ensure that they are properly aligned, ``X``, ``pred_times`` and ``eval_times`` are required to be pandas DataFrames/Series sharing the same index.\n\nCheck the docstrings of the cross-validation classes for more information.\n\n",
        "description_content_type": "",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/sam31415/timeseriescv",
        "keywords": "machine-learning cross-validation scikit-learn time-series",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "timeseriescv",
        "package_url": "https://pypi.org/project/timeseriescv/",
        "platform": "",
        "project_url": "https://pypi.org/project/timeseriescv/",
        "project_urls": {
            "Homepage": "https://github.com/sam31415/timeseriescv"
        },
        "release_url": "https://pypi.org/project/timeseriescv/0.2/",
        "requires_dist": null,
        "requires_python": "",
        "summary": "Scikit-learn style cross-validation classes for time series data",
        "version": "0.2"
    },
    "last_serial": 4249786,
    "releases": {
        "0.1": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "bc64557af0bec1811fbf18d46a6f130d",
                    "sha256": "15c249987d870fa1c0d926c57e51203247891f81082e7709c6da9eb8d7ec1df7"
                },
                "downloads": -1,
                "filename": "timeseriescv-0.1.tar.gz",
                "has_sig": false,
                "md5_digest": "bc64557af0bec1811fbf18d46a6f130d",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 5980,
                "upload_time": "2018-09-06T22:21:40",
                "url": "https://files.pythonhosted.org/packages/da/83/ab312c12618f1b0f6d16cc0c86fd28d829d0f8acfb221bd12a8228a4d016/timeseriescv-0.1.tar.gz"
            }
        ],
        "0.2": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "87787d78e9af155d7f41426f5b402905",
                    "sha256": "6937e6b10eb5c7dcc0cf790f4c40b6abdcf925f1f67358f917d605a2f8c5cf5e"
                },
                "downloads": -1,
                "filename": "timeseriescv-0.2.tar.gz",
                "has_sig": false,
                "md5_digest": "87787d78e9af155d7f41426f5b402905",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 6733,
                "upload_time": "2018-09-07T19:42:59",
                "url": "https://files.pythonhosted.org/packages/a2/13/37fe80a5f6ddac54899ad4ccc4aea1a838a1d7bc2b9342bd4a2a42ea0680/timeseriescv-0.2.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "87787d78e9af155d7f41426f5b402905",
                "sha256": "6937e6b10eb5c7dcc0cf790f4c40b6abdcf925f1f67358f917d605a2f8c5cf5e"
            },
            "downloads": -1,
            "filename": "timeseriescv-0.2.tar.gz",
            "has_sig": false,
            "md5_digest": "87787d78e9af155d7f41426f5b402905",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 6733,
            "upload_time": "2018-09-07T19:42:59",
            "url": "https://files.pythonhosted.org/packages/a2/13/37fe80a5f6ddac54899ad4ccc4aea1a838a1d7bc2b9342bd4a2a42ea0680/timeseriescv-0.2.tar.gz"
        }
    ]
}