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Time Series Processing Using Regression

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# Precessing timeseries problems using Regression This is a simple framework to process multi-variable timeseries dataset using regression.<br>

Most time series analysis methods focus on single variable data. It’s simple to understand<br> and work with such data. But sometimes our time series dataset may containe multi-varibles.<br> For example, in marketing analysis, profit of a day may not only be decided by the number<br> of customers, but also depend on campaign, CM and so on.<br> It is harder to model such problems and often many of the classical methods do not perform<br> well.<br>

Since regression methods are good at processing multivarible, we can simply turn our timeseries<br> dataset into training dataset for regression by exluding time columns.<br>

## Restrictions In general when using regression methods, timeseries data for your independent variables must be<br> avaliable to make predicitons.<br>

## How to use See example.ipynb

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