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        "author": "Jiajun Zhu",
        "author_email": "george.choo@outlook.com",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
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        "description": "# Precessing timeseries problems using Regression\nThis is a simple framework to process multi-variable timeseries dataset using regression.<br>\n\nMost time series analysis methods focus on single variable data. It's simple to understand<br>\nand work with such data. But sometimes our time series dataset may containe multi-varibles.<br>\nFor example, in marketing analysis, profit of a day may not only be decided by the number<br>\nof customers, but also depend on campaign, CM and so on.<br>\nIt is harder to model such problems and often many of the classical methods do not perform<br>\nwell.<br>\n\nSince regression methods are good at processing multivarible, we can simply turn our timeseries<br>\ndataset into training dataset for regression by exluding time columns.<br>\n\n## Restrictions\nIn general when using regression methods, timeseries data for your independent variables must be<br>\navaliable to make predicitons.<br>\n\n## How to use\nSee example.ipynb",
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